With AI being increasingly used for go-to-market, the letters “MCP” have found their way into more LinkedIn, Reddit, and Substack feeds when it comes to lead generation. But why, and should you even care? 

While it sounds like a term that firmly belongs in the developer’s world, if you run your own outreach, it’s time to pay attention. 

Prospecting is changing, and MCP is the key to connecting your Hunter go-to-market data into chat-based prospecting in Claude, ChatGPT, and Perplexity.

The short story:

• An MCP (Model Context Protocol) connection is the standard plug that lets an AI assistant like Claude, ChatGPT, or Perplexity use your Hunter account from a conversation. You ask in plain English, and the assistant does the Hunter work for you, meaning you don’t need to use the Hunter app interface..
• An API is the counter where one piece of software asks another for something specific. Hunter's APIs answer questions like "who works at this company?" and "is this email address safe to send to?"
• The MCP sits in front of Hunter's full API, meaning one connection gives your AI assistant 30+ tools to identify companies, find and verify email addresses, enrich contacts, and manage your leads and email sequences.
• The biggest benefit is that it prevents an AI assistant from hallucinating data. With an MCP connection, every contact comes back found and verified, or flagged as something Hunter couldn't confirm, so you never send to an invented email address.
• The MCP is included in every Hunter plan. Follow these quick start instructions:
• Connect Hunter to Claude by adding the .md file inside a Claude chat
• Connect Hunter to the ChatGPT app by adding the .md file inside a ChatGPT chat

What is an MCP?

MCP stands for Model Context Protocol. It's an open standard, published by Anthropic (the company behind Claude) in 2024, that gives AI assistants one shared way to connect to outside tools and data.

The comparison often used is the wall socket. 

Before wall sockets were standardized, every appliance needed its own wiring job. Now any plug fits any socket, and nobody thinks about it. MCP does the same for AI assistants: any tool that speaks the protocol can connect to any assistant that speaks it, without a custom integration built for each pairing.

This is important because while AI assistants are smart, they lack access to your specific data.

Claude can describe a great-fit customer with ease. But, it cannot identify the companies and verified email addresses of decision makers inside. 

AI assistants’ knowledge stops on the data it is trained on. That’s why you need to connect your hunter.io account as a key to unlock your lead generation.

Once Hunter’s MCPis plugged in, your AI assistant can search companies, find contacts, verify addresses, and save leads using your actual account, with your plan and your credits, from inside the conversation you're already having.

You don't need to understand how the protocol works underneath, any more than you need to understand the wiring behind a socket. 

You set it up once and forget it exists.

What is an API?

An API (Application Programming Interface) is the counter where one piece of software asks another for something specific. It's a published menu of requests the service will answer, each with a defined question and a defined answer. 

Hunter's Email Verifier API, for example, takes one email address and returns a verdict: valid or not, with a confidence score and the detail behind it. 

The Domain Search API takes a company's website and returns the email addresses Hunter has for the people who work there, with sources attached.

APIs are precise, fast, and reliable, which is exactly why products get built on them. 

Unfortunately, APIs have long sat in the world of developers, with the ability to write code an immediate barrier to those less-technically minded.

The MCP eliminates that barrier.

How does an API and MCP work together?

If the Hunter API is the service counter, the MCP server is the translator standing at it on your behalf.

Breaking down the MCP and API
You type something like "find the marketing decision-makers at these 20 companies and verify their email addresses" into Claude. 

Claude decides which tools to use, whether that's a Domain Search, an Email Verifier check, or something else entirely, instead of you needing to think about the right solution. 

The MCP server takes each request and places it at Hunter's API counter in the format the API expects. 

It then collects the responses and hands them back to the assistant, which turns the raw data into the table you actually wanted.

The MCP gives you the access without tech knowledge
The API does the work it has always done, and the MCP adds the translation in front, letting you place orders in plain English instead of code. 

One connection covers the whole counter, because Hunter's MCP exposes the full Hunter API as over 30 individual tools, and the AI assistant picks the right ones for whatever you ask.

Staying in control of your Hunter credit usage
Critically, you remain in control of the credits you use.

In Claude, each tool allows you to set a permission level, so you can let read-only lookups run freely while anything that changes your account or uses credits waits for your approval. 

Verifying an email costs 0.5 credits and saving a lead costs 1 credit, and with the recommended settings, neither happens without you saying yes.

What APIs are available from Hunter?

Since the MCP is a window onto the API, it helps to know what's actually on the menu. 

Hunter's API breaks into groups.

Find companies and people

Discover API
The Discover API finds companies matching your criteria, whether you describe them in natural language or with filters for industry, size, location, and technology, and it's free to call. 

The Email Count endpoint tells you how many addresses Hunter has on file for a company before you spend anything. 

Domain Search API
The Domain Search API returns the email addresses found for a company, with names, roles, and the public sources each one came from (1 credit per 1-10 addresses). 

Domain Search API
The Email Finder API takes a person's name and their company's website and returns their most likely professional address with a confidence score (1 credit).

Verify and enrich data

Email Verifier API
The Email Verifier API checks if an address is deliverable and returns a status and score, at 0.5 credits per check. It ensures you find verified and accept-all data to improve your bounce rate.

Lead Enrichment API
Lead Enrichment turns an email address into a person: name, role, location, and social profiles. 

Company Enrichment API
Company Enrichment turns a website into company detail: industry, size, description, and the technology it runs on. 

Combined Enrichment API
Combined Enrichment does both from a single address. Each costs 0.2 credits, and only when the core data actually comes back.

If you used Clearbit for enrichment before it was folded into HubSpot, Hunter's API works as a direct replacement, down to a Clearbit-format response option. 

Logo API
The free Logo API returns any company's logo from its website address, ready to be used in your CRM, landing pages, dashboards, or product.

Manage leads and send emails

Leads API
The Leads API and its neighbors (Lead Lists, Custom Attributes) let you save, organize, and update the contacts you've found. 

This enables you to segment your contacts and companies based on criteria like location, technology, owner, or whatever attribute you choose.

Sequences API
The Sequences API adds recipients to your outreach sequences and starts them. 

API key basics

If you want to go deeper, the full API documentation covers every endpoint with examples. 

Your API key lives at hunter.io/api-keys, and it should be treated the same as any password, meaning you never share it or paste it anywhere public. 

An Account endpoint reports your plan, credits, and usage, free of charge, which is how your AI assistant can tell you whether a big job fits your remaining credits before it starts.

For experimenting without spending credits, the special key test-api-key returns dummy responses on the Domain Search, Email Finder, and Email Verifier endpoints. 

If you connect through Claude's connector settings or the ChatGPT app, you may never touch a key at all, because the connection uses OAuth: you log in on Hunter's own page, and the assistant never sees or stores your password.

How is the Hunter MCP being used?

If you're handling go-to-market by yourself, the MCP can be the foundation for an entire Claude prospecting workflow. 

Claude can act as a research agent, identifying great-fit companies, finding the contacts with decision-making power, and then using Hunter to get their verified email addresses. 

Users work this way because it blends high-quality, trustworthy data with less context switching and browser hopping, the kind that wastes time and pulls you away from other work. 

The AI assistant does the reasoning, Hunter gives it the data you can trust, and you can work on something else all the while you’re running your lead generation. 

Here's what that can look like in each assistant.

If you're handling go-to-market by yourself, the MCP can be the foundation for an entire Claude prospecting workflow. 

Claude can act as a research agent, identifying great-fit companies, finding the contacts with decision-making power, and then using Hunter to get their verified email addresses. 

Users work this way because it blends high-quality, trustworthy data with less context switching and browser hopping, the kind that wastes time and pulls you away from other work. 

The AI assistant does the reasoning, Hunter gives it the data you can trust, and you can work on something else all the while you’re running your lead generation. 

Here's what that can look like in each assistant.

Claude

Claude's strength is reasoning over everything at once. 

Paste in the websites of customers you've already closed and ask it to work out the profile they share, then have Hunter find companies matching that pattern. 

Ask it to score every match against the profile with a reason per score. 

Then, have Hunter turn the top companies into named decision-makers with verified addresses, and finish with a one-line outreach hook per contact. 

Claude can even build the results into an interactive account-scoring sheet you can sort, edit, and reopen later. 

The connector works on Claude's Free, Pro, and Max plans, and on the Free plan Hunter can be the one custom connector you're allowed.

Click here to learn more about Claude x Hunter.

ChatGPT

The Hunter ChatGPT app suits starting from a customer you already win with. 

Give it one website and ask for lookalike companies, and Hunter Discover returns a ranked list with the number of addresses on file for each. 

Narrow it in a sentence ("just the European ones"), ask which recently raised money or are hiring, then run a Domain Search to get named contacts with verification statuses, and have it draft a three-sentence opener for the first one. 

The process runs on ChatGPT's free plan; you call the app by typing @ and then Hunter.

Click here to learn more about ChatGPT x Hunter.

Perplexity

Perplexity reads the open web as it is today and cites where each claim came from, so it's great to use when you're building a lead list from start, for instance with companies that raised this quarter.

It builds the market map with sources attached, reasons about which companies fit what you sell, and then hands the qualified list to Hunter for named contacts and verified addresses. 

One thing to know upfront is that custom connectors need a paid Perplexity plan, Pro or higher, so the free tier can't add Hunter yet.

Click here to use Hunter in Perplexity.

Prompts to test your MCP

Whatever your AI assistant, once set up, try these prompts:

• "Before we start, pull my Hunter account details: the plan I'm on, what I've used this month, and what's left. Then estimate what this job will use and tell me whether it fits."
• "These are deals I lost a year ago and want another run at. Verify each contact's email with Hunter. Where an address comes back invalid, the person has probably moved on, so run a Domain Search on the company and find whoever now holds the role."
• "Pull the stats for every sequence I sent this quarter and rank them by reply rate. Name the winner and the loser, and tell me what separated them."

Why use Hunter's MCP?

Broadly, the Hunter MCP will enable you to trust the business data you're using to orchestrate outbound in an AI assistant:

More specifically, the MCP's benefits spread across four areas:

1) Data hallucinations
Ask an AI assistant for someone's email address and it will often produce one that looks right and doesn't exist, because guessing plausible text is what these models do. 

Send to enough of those and your bounce rate climbs, and past 2% your sender reputation starts paying for it. 

With the MCP connected, the assistant's answers come from Hunter's records instead of its imagination. 

Addresses arrive with a verification status and confidence score, accept-all servers get flagged as the warning they are, and where Hunter has nothing, it says "no data" rather than inventing a name. 

Knowing exactly which contacts you can send to today, and which need more work first, is the whole game in deliverability.

2) Limit context switching
The old productivity loop was research in one window, Hunter in another, a spreadsheet in a third, and CSV exports moving the same names between them. 

Contact data goes stale quickly (22% of email data decays each year), so every manual hop is also a chance for the list to age before you use it. 

With the MCP in place, a contact moves from research to a verified leads list to a sequence inside one conversation, and the re-verify step is a sentence, not an export.

3) No added cost
The MCP connection costs nothing extra. It's included in every Hunter plan, your existing credits and limits apply, and a free Hunter account works from day one. 

4) You're in control
Unlike AI assistants, whose token usage is something you have to actively control by selecting a different model, reasoning level, and/or choose to use the assistant in a browser/desktop app, the Hunter MCP gives you full control over Hunter credit usage. 

It's built to keep you in control, with OAuth sign-in and per-tool permissions, so the assistant asks before it does anything that spends credits or changes your account.

Go beyond the MCP

The MCP is the newest door into Hunter's API, but far from the only one. In fact, Hunter’s API is being used to grow businesses across outreach, products, and marketing experiences.

Build products
Vocations, a job-signal platform for recruiters, uses the Email Finder API to power the contact feature at the heart of its product. The integration went live in a day, and today its 300 daily users find around 1,500 decision-makers through it.

Make verification part of the prospecting routine
Modgility, a RevOps consultancy with a two-person sales team, builds lists of companies running HubSpot, verifies the contacts, and runs targeted sequences. That outbound accounts for 25% of their new business.

Feed your CRM
WebFootprint, a software agency selling to fewer than 200 possible buyers nationwide, pipes Hunter's data into a custom CRM, where it fills every company and contact record and surfaces new hires at target accounts automatically.

The founder puts the savings at an hour a day, and he's been a Hunter customer since 2016.

Those are very different businesses placing orders at the same counter. 

The MCP simply means you no longer need an engineering background to place one yourself.

Use the Hunter MCP

The fastest way to connect Hunter is fitting - let the AI assistant handle its own setup.

We've written step-by-step instructions designed to be pasted directly into a chat. 

The assistant reads them, greets you, and walks you through the whole connection one confirmation at a time, from checking you have a Hunter account through to running your first real prompt.

 You won't have to figure anything out on your own.

• Claude: Copy the Hunter for Claude setup instructions here into a new Claude chat.
• ChatGPT: Copy the Hunter for ChatGPT setup instructions here into a new ChatGPT chat.
• Perplexity: On a Pro plan or higher, add Hunter as a custom connector, then run your first market map.

The setup takes five minutes, and the next time you need a list built, checked, and saved, you'll do it without opening a single extra tab.

Learn more about the MCP

To learn more about the MCP, APIs, and Hunter in your AI assistant, visit our help center content below:

• How to connect Hunter to Claude with MCP, the full walkthrough with screenshots, including the API-key route for Claude Team members without admin rights.
• The Hunter API, every endpoint with credit costs and rate limits.
• Can I use a test API key? for experimenting without spending credits.
• How to verify emails via API, with code examples, if you're ready to write a little code.
• Which plan is right for me? on choosing between the All-in-One Outreach plans and the Data Platform plans.

MCP FAQs

Do I need a paid Hunter plan to use the MCP?

No. The connection works with every Hunter plan, including free accounts. Your existing credits and request limits apply, and some actions use credits, like verifying an email (0.5 credits) or saving a lead (1 credit).

Do I need to be a developer?

Not for the MCP. Connecting through Claude's settings or the ChatGPT app is a few clicks plus a Hunter login, and from then on everything happens in plain English. The API itself is the developer route, for building Hunter's data into a product or an internal system.

What's the difference between the Hunter MCP and the Hunter API?

The API is the underlying service: the set of requests Hunter answers, from Domain Search through to Sequences. The MCP puts a translation in front of that API so an AI assistant can use it through conversation. It's the same data and the same credits, reached a different way.

Which AI assistants does it work with?

Claude (web, desktop, and Claude Code), ChatGPT via the Hunter app, Perplexity on its paid plans, and any other assistant that supports MCP connectors.

Is my data secure?

The connection runs through Hunter's official MCP server using OAuth, meaning you log in on Hunter's own page and the assistant never stores your password. The assistant only accesses your Hunter data within the permissions you grant, and you can revoke the connection at any time.

What can the assistant actually do once connected?

The connection covers the full Hunter API in over 30 tools: Discover, Domain Search, the Email Finder, the Email Verifier, person and company enrichment, Leads with lists, tags, and custom attributes, message templates, and Sequences through to their statistics.

Will the assistant spend my credits without asking?

Not if you set it up the recommended way. Hunter suggests leaving read-only tools free to run while setting anything that writes or spends to "needs approval," so every credit-using action waits for your yes.

When you’ve received an inbound lead and prep for a call, it’s likely the lead’s information is incomplete or, at worst, missing.

With this prompt for ChatGPT or Claude, you can use Hunter to prepare for your sales call and stand out for all the right reasons with your lead.

Why this matters

Pre-call research gets skipped because it can feel like another admin task. But, there are risks with not thoroughly preparing for a call. 

Those reasons are endless, but can vary from the email address is wrong, or the person no longer works at the company, to not realizing that additional attendees are added to your call, - and you could have known enough about them to alter your talk track.

You find out afterward, if you find out at all.

The research that gets done before a call is the research that fits into a coffee break. Anything longer gets postponed until the meeting has already happened.

Here's how to build that briefing in your AI assistant with Hunter.

Your plan

• What you'll achieve: A view of who matters in the department you're meeting, verified email addresses for the handful worth contacting, and a first draft of your outreach that doesn't announce itself as machine-written.
• Where this runs: In ChatGPT or Claude, using the Hunter connector. The prompts below are identical in both, so use whichever you already have open.
• What it costs: Hunter can be used in your AI assistant as part of your Hunter plan. Credit-based actions like finding and verifying addresses use your account balance.
• Time to complete: 15 minutes.
• When to run this play: You have a call booked and you need to run research.
• When to run another play: You already know the room and only need the follow-up written, so skip to Step 3.

Step 1: Connect Hunter to your AI assistant

Use the guide below to know where you to place your next step:

Connect Hunter to whichever AI assistant you use. 

Setup takes five minutes:

• Read the ChatGPT guide
• Read the Claude guide.

Have one useful thing ready to send to the contacts you’ll find.

A benchmark or a short case study that answers a question they're likely to have. 

You'll need this asset in Step 3, and picking it in the moment is how people end up attaching nothing.

Learn more about using different lead magnets with our outreach planner

Step 2: Build the briefing

Open your AI assistant and enter the below, replacing the placeholders:

I have a call with {{company_name}} tomorrow about {{reason for call}} . Pull together a briefing: find the company's website and key details, identify the decision-makers in {{department}} (especially anyone with “{{seniority_level}}” in their title), verify their email addresses, and surface any recent context I should know.

Here's an example prompt for prepping for a call with a marketer from Forrester about their design requirements:

Two things to be wary of:

• Ensure you have the right department: This can be an issue when a company splits Growth and Marketing, or runs RevOps separately from Sales. Tell your AI assistant which one you're meeting.
• Someone you know is joining doesn't appear in the results: Name them and ask the AI assistant to find and verify them too.

Review the results of the initial company snapshot:

Because you gave the AI context on the call, it will provide initial research that you can bring into your conversation:

With this information, you'll then receive a digest of how your solution fits into their stance on AI GTM:

Step 3: Verify and save only who you need

With the results returned from your AI assistant, review the list of contacts and the advice that Hunter provides you:

Hunter will, if appropriate, suggest a second contact that could be useful to broaden your preparation:

Use this prompt to select the leads you want to save from this search:

Save the verified contacts to a new Hunter Leads list called {{list_name}}, and tell me how many credits that will use before you do it. For anyone in {{department}} without a verified address, run a Domain Search and tell me who holds the role now.

Remember, you really need one-two people, maximum, to drive your chances of a reply rate up.

Before saving the contacts, the AI assistant will ask for permission:

Ask the AI assistant to flag anyone whose address comes back as accept-all rather than valid.

Those aren't wrong, but they bounce far more often than verified addresses, so keep them out of anything that matters before a meeting.with our outreach planner

Follow the URL and you'll see your saved list inside your Hunter account:

Step 4: Draft the outreach

Now enter the below prompt for assistance in writing your messages to the leads.

Write two drafts to send before the call - one for [person 1], one for [person 2], and one for [person 3]: an email under 100 words and a LinkedIn note under 60. Both should reference the meeting and share a link to our portfolio of example work at www.aidified.io/work. Ensure that the email is relevant to the contact's job title and leverages the pre-call preparation you have helped me generate. If you make an observation that comes from from the research, ensure it's a natural connection between this observation and the connection to my work. Rules: no em dashes, no “it's not X, it's Y” constructions, no phrases like “in today's landscape” or “let's dive in”, and no words like “resonate” or “leverage”.  Use contractions. American English. Ask one question at the end, not three. 

Personalized for this campaign, we'll add the below context:

Next, review each of the email and LinkedIn requests generated. Ensure that they're personal to the lead, and manually edit where necessary:

It’s important to ensure your messages are relevant to the lead, as 69% of decision makers are bothered when your content shows signs of AI. Don’t make the use of AI blind them of your value and dismiss you before you can talk to them.

Use LinkedIn as a follow-up channel, referencing your email, to give the lead a choice of where they want to engage with you.

Once the content is edited, you can send these messages through Sequences, naming each Sequence as the person's name + Forrester pre-call intro.

What this looks like when it's working

You join the call already knowing who the other three people are, you've shared examples of your work and tied this into their stance on AI, and you come across as prepared, thoughtful, and professional.

Your follow-up goes out the same afternoon to addresses that work, and it sounds like you.

If the briefing comes back thin, that usually means a small company with little public footprint. 

Run Step 2 anyway, since a verified address for one person beats a rich profile of nobody.

Put all of this into action today with Hunter's Outreach Planner:

Reading this in the summer? You have until Labor Day to get your lists ready if your recipients are in the Americas.

We analyzed 5 years worth of data and yes: there is some seasonality to email outreach.

However:

• The effect is smaller than you might suspect,
• It depends on the region you operate in, and
• You should still send outreach year-round.

In this article, we'll show you what we found and what you need to do about it to maximize replies throughout the year.

How we measured it

We looked at 14,500 senders and 86,000 sender-months from 2021 through 2025: sales teams, agencies, recruiters, and PR.

The context:

• Average reply rates have been dropping. Blame channel saturation or stricter spam filtering. Either way, the average sender's reply rate fell by roughly 45% over these five years, so we can't naively compare reply rates per month.
• The mix of senders changes constantly, and a few outliers can drag a month's average around on their own.

We accounted for it:

• Every sender is compared only to themselves. October is "good" if senders did better than their own typical month (on average).
• The overall decline in reply rates is accounted for before months get judged.
• One sender, one vote, whether you sent 60 emails or 300,000 that month.
• A month only counts if it repeats. One great October proves nothing. Five in a row is a pattern.

What five years of data says

If you're sending in the Americas, don't sleep on the September-October window. October wins even though it sits inside the busiest sending period of the year.

September-October was the best window to send from 2021-2025, with about 8% more replies in October than the average sender's normal. To put that in real terms: if your usual reply rate is 2.5%, October may bring in about 2.7%. Not groundbreaking, but worth planning around.

Then the window shuts toward the end of Q4... as expected. November and December came in below normal in 4 of the last 5 years, at about -5%.

If your recipients are in Europe, the calendar is more of an excuse. No month was dependably good or bad for EMEA senders in five years. Even the sacred August-holiday intuition doesn't show up reliably in the data.

Asia-Pacific is messier. October was above normal there in all five years too, so the autumn window seems to carry over. But treat the region's averages with care: January looked like APAC's best month for four years, then came in well below normal in early 2026. APAC is our smallest sample, too.

One caveat before you circle October on the calendar: the direction repeats, the magnitude doesn't. October's edge ranged from +4-16% depending on the year. Treat these as tendencies, not guarantees.

Everyone shows up a month late

When we charted send volume against reply rates week by week, the two peaks don't line up.

Reply rates peak from late September through the first week of November. Send volume peaks in mid-November, right after replies fall off a cliff.

January is the mirror image. The first weeks of the year get less volume than an average week, while the people you're writing to are already back at their desks. December's quiet inbox everyone plans around? It actually happens in the first half of January, and almost nobody uses it.

Best for replies ≠ most important

This is where I'll argue against our own data.

October being your best reply month doesn't make it your most important sending month. That depends on your sales cycle, your industry, and when you actually need the revenue.

Do the math backwards. If your sales cycle is 90 days, replies you get in October become deals that close in January.

The same math works for recruiting: with a 60-day time-to-fill, October outreach fills January seats.

And if you run campaigns for clients, the "important month" is different for every account on your book.

The easy months and the important months are usually different months. And if you carry a quota, you already know that an easy quarter isn't where your effort should go.

The salesperson who looked relaxed in March did the work in November, when reply rates were at their worst and everyone else had mentally checked out.

Which is the real lesson here: email outreach builds pipeline. Closing happens somewhere else, on someone else's timeline.

What do you do with the strong months?

An 8% better month pays in proportion to what you send, and sending capacity is the one thing you can't create overnight. A new email account needs weeks of warmup before it can carry real volume, and a new domain needs longer. If you want more mailboxes working for you in October, they need to exist by August. You can buy domains and email accounts right inside Hunter, preconfigured for cold outreach, so the setup doesn't eat your September.

Then there's the list itself. Build it early and run it through Email Verifier, because extra volume on an unverified list just means extra bounces at the worst possible time, when your fresh accounts are still earning their reputation.

The window multiplies whatever you bring into it. Bring more.

What do you do with the weak months?

Not nothing.

The data says November and December replies are harder to get with the same playbook everyone else is running. But:

• It doesn't say recipients stop being people.
• The drop didn't happen every single year.
• At worst, the reply rate is about 10 points lower.

Send something with no ask. A useful benchmark, or a genuinely relevant "this reminded me of your launch" note. A December email has one job: to make January's email warm.

Book Q1 while everyone else chases Q4. "Let's talk in January" is an easy yes in a month when every other email is begging for 15 minutes before the holidays.

And do the unglamorous work. Build your lists and verify them. If you send at volume, this is also the time to warm up mailboxes and test copy on small batches. The mid-September window rewards whoever prepared in August. The January restart rewards whoever prepared in December.

This is exactly what we built the Outreach Planner for: a free 12-month calendar that gives every month a job, telling you which weeks to build, verify, warm up, and push, and what angle fits the season when the default pitch stops working. It exists so you don't send the same email in week 50 that you sent in week 38 and expect week-38 results.

The one-line version

If your market is in the Americas, the six weeks from mid-September to the end of October are the best sending window in five years of data. Plan so your lists are ready before it opens. Everywhere else, and every other month, the calendar is a tiebreaker.

Because the best month in five years of data buys you +8%. A better list, a sharper offer, and a healthy sending setup are worth multiples of that in any month.

The calendar decides when replies come easy. Whether your email deserves one is up to you.

At Hunter, we've been skeptical about AI taking over outbound.

Turns out we were right. And wrong.

Right, because it never took over. Outbound doesn't run itself even with AI.

Wrong, because AI gradually got good enough to assist with every part of the process: building lists, verifying contacts, writing sequences, analyzing what worked.

Autonomous SDRs have been the wrong direction for applying this technology. MCPs have proven themselves to be the right direction: a skilled human operator assisted by AI enabled by data, using an outreach platform via MCP.

And the operator's "skill" has nothing to do with the technicalities of outbound anymore. It has everything to do with understanding the business, the customer, and the strategy.

This guide covers AI lead generation, or how we at Hunter think you should think about applying AI to email outreach.

What is AI lead generation?

AI lead generation means using natural language with AI enabled by live data to automate how you find leads, verify their contact information, and reach out to them.

You describe who you want to reach and why; the AI executes the steps that used to be manual. You give examples of sequences that worked before; AI scales them to other industries in minutes. You say that you want to find low-hanging fruit in your lead list; AI, informed by your strategy, finds leads to contact next week.

AI doesn't replace your judgment or act autonomously on the critical parts of the process. It's just a tool, the same way your CRM is a tool. It just happens to be a tool you can talk to.

AI lead generation covers most of the lead generation workload:

• Building a strategy to find and acquire customers
• Analyzing competitors and adjacent markets for targeting ideas
• Mining your CRM for patterns and missed opportunities
• Creating lead magnets and content around target accounts
• Running email outreach and follow-ups
• Closing the loop: analyzing what worked and deciding what to test next
• Pre-call research and meeting notes

If all you have at this point is doubt about whether this can really work at all, consider two things:

First, context. You can point an LLM at your website, your messaging, your case studies, or your past campaigns, and it will work from your reality instead of internet averages. It can even learn from your unrelated chats. If you're a founder working with AI on all the other parts of your business, it already knows where your value gap sits and who you're building for.

Second, data. LLMs don't know anyone's email address. They were never trained on verified contact data, and they never will be for privacy reasons. Connect it to a source that actually knows (this is where Hunter comes in), and the model's reasoning finally has facts to use.

How is AI lead generation different from traditional prospecting?

Traditional outbound has always been capped by human capacity. Every step was somebody's hours. Want more pipeline? Hire more SDRs, buy more tools, define more process. And everything stops when the workday does.

Not only that, AI doesn't suffer from the limiting beliefs you or your team may hold about who you should be contacting and what's possible with email. While it won't outsmart you about your own business, it can act like a consultant, providing alternative perspectives about anything from markets to prioritize to what your follow-ups should say.

With AI, the steps are the same ones sales teams have always run. What changes is that one operator can now do more, and the parts that don't require judgment no longer wait for you to be at your desk.

How to set up AI lead generation with Hunter

The setup for AI lead gen is smaller than you'd expect. You need a Hunter account, and an account with ChatGPT or Claude or Perplexity—or a custom setup using the Hunter API if you prefer.

Step 1: Connect Hunter to your AI

Pick the route that matches where you work:

• ChatGPT: install the Hunter app
• Claude: add the Hunter plugin
• Perplexity, or any other client that speaks MCP: connect Hunter's MCP
• Custom setups: the Hunter API directly

Once connected, Hunter's search, verification, enrichment, and campaign tools are available in every conversation. No exports, no tab-switching.

Step 2: Run your first tasks in conversation

Here are 7 starter prompts, each a real task off your plate today. This will show you what's possible:

1. Build a link-building prospect list: "Find sites and blogs in [niche], based in [location], that publish content about [topic]. Find and verify the email addresses of their editorial or marketing contacts and return the results as a list."
2. Find the right editor to pitch: "I want to pitch [story angle] to [publication]. Search Hunter for editorial contacts there and identify the most relevant person based on title and beat."
3. Enrich a contact list: "For this list of companies and contacts, find and verify each email address with its confidence score. Mark anything you can't verify as 'not found'."
4. Verify a list before sending: "Run each address below through the Email Verifier and flag anything risky or undeliverable."
5. Qualify an inbound lead: "Look up the domain of this email address, verify it, and find related contacts at the company."
6. Prep for a sales call: "I have a call with [company] today. Find the key decision makers I should know about before speaking with [contact name]."
7. Analyze your outreach: "Look at [campaigns] in Hunter and tell me what's working, what isn't, and define an A/B test for the subject line of the next sequence."

If a prompt returns something you'd have spent an hour on, you've just confirmed the whole premise.

Step 3: Turn what works into routines

When a prompt earns its third run, stop retyping it. Save it as a Skill (Claude), a GPT (ChatGPT), or a Gem (Gemini): reusable instructions with your rules baked in—"verified or accept-all addresses only," "always find at least 2 contacts per company," "never start a sequence without my approval."

Then give those instructions a schedule in an agent environment (Claude's Cowork, ChatGPT's workspace agents, Perplexity's Computer) and add your context: ICP, messaging, case studies, past campaign results. Budget 2-3 hours for this, once. From then on, the routine runs while you work on something else, and pings you when a decision needs a human.

That's the full architecture: a connection, a handful of prompts you trust, and a schedule.

What the Hunter MCP is capable of

The easy assumption about "Hunter in your AI" is that it's a search box: ask for an email address, get an email address.

It's closer to handing your AI the keys to the platform. Connected via MCP, your LLM can operate every stage of the outbound workflow through plain conversation:

• Search the B2B database. Describe companies in natural language—"fintech startups in France with 11-50 employees"—and get matching companies from Discover. Before spending anything, the AI can check how many named contacts Hunter holds at each company, so you size a prospecting batch before committing to it.
• Get and verify contacts. Pull the published contacts at any domain, filtered by department or seniority. Find a specific person from a name and a domain. Verify any address and get its deliverability status with a confidence score.
• Enrich people and companies. From an email address or a LinkedIn handle: role, company, industry, headcount, technologies used, funding, social profiles.
• Manage your leads. Create leads and lists, tag and segment them, add custom attributes, move and merge in bulk. Existing leads are never overwritten.
• Run sequences. Draft multi-step campaigns, set follow-up delays and sending windows, add recipients, start, pause, resume—and pull per-sequence stats to see what worked.
• Sync your stack. Push leads to HubSpot, Salesforce, Pipedrive, or Zoho. Check your plan, credit balance, and usage before kicking off a batch.

Notice what this means: the AI can take real actions with real consequences. Which is why the guardrails live in the MCP itself, not in a prompt asking the model to be careful.

Every operation is labeled free or credit-consuming, and credits are only charged when Hunter actually finds something. Bulk operations return a cost estimate and stop until you approve it. Deletes state the affected count and wait for confirmation. And starting a sequence—the moment real emails go to real people—always requires an explicit yes from you. Not because the model promised to check in, but because the API won't act without it.

The model brings the reasoning. The MCP brings the facts—and the brakes.

The benefits of AI lead generation

1. Better leads, not just faster leads

Here are some examples of when this is true:

Every address gets verified before it enters a list, because verification is part of the routine, not a cleanup you run when bounce rates scare you.

Prioritization runs on evidence—engagement from past campaigns, companies that match your best customers—instead of whoever happened to look promising that morning.

And every email draft starts from enrichment data about the actual company, which is the difference between personalization and a first-name merge tag.

2. The math works at any scale

If you're currently paying an agency or considering an SDR hire, the comparison is short: an agency retainer runs thousands per month and the cost-per-lead is hard to sustain unless your contract values are high. Hunter plus an LLM subscription is a rounding error next to that.

But the math also works if you're comparing against doing it yourself by hand. Even at 30 emails a month, research, verification, and enrichment eat most of the hours—and those are exactly the steps the AI takes over. You keep the parts that were never the bottleneck: judgment and conversations.

There's a quieter saving too. When an agency relationship ends, everything it learned about your market walks out the door. Skills, prompts, and context files stay yours.

3. You prospect where you already work

Hunter has always followed this principle. The Chrome extension, the CRM integrations... and the MCP extends it to your AI.

In the app, you have to know Hunter to use Hunter: which feature does what, in which order. In a conversation with your agent, you describe the outcome ("50 marketing directors at US SaaS companies under 100 people, verified emails only") and the AI translates. Your strategy docs, your ICP, your past conversations about what worked are all context the AI carries into execution.

The limits of AI lead generation

1. You're still needed

In a chat, you're present for every step by definition. An agent reduces that, but doesn't remove it: it SHOULD stop and ask before consuming credits, before modifying your lists, and before sending anything. An agent that emails people without your explicit yes is a liability.

So budget real time for review. The work shifts from doing to checking, but it doesn't disappear.

2. Hallucination, or why the data layer isn't optional

An LLM's context window is its working memory, and it's finite. Long lead-gen conversations outgrow it, older details fall away, and when the model is missing a fact, it doesn't say "I don't know." It fills the gap. In lead generation this is fatal.

3. AI slop

Models are trained on averages, and the email outreach average is terrible. Left unshaped, AI reproduces the clichés it learned from: "I hope this email finds you well," fake personalization, the pitch that sounds like every other pitch in the inbox.

Recipients don't care whether you used AI. They care whether it reads like AI. Feed it examples of your actual writing, and consider a Skill that checks every draft against a cliché list before you ever see it.

4. Models change under you

Providers update models constantly, and output quality shifts with them—usually for the better, sometimes just differently in ways that matter for your copy.

Get started with Hunter in your AI

Your AI can now run every mechanical step of outbound through plain conversation, on real data instead of confident guesses. It stops and asks before anything that spends money or reaches a human. Which means the only irreplaceable skill left in lead generation is the one you already have: knowing your business, your customer, and your strategy. Try running your next Sequence with AI, enabled with the Hunter MCP.

Connect Hunter to your AI:

• Hunter in ChatGPT
• Hunter in Claude
• Hunter in Perplexity
• Hunter's MCP

Go deeper on specific use cases:

• How to prospect inside ChatGPT with Hunter
• How to use Hunter with Claude for prospecting
• Research-led prospecting with Hunter and Perplexity
• How to use AI in your cold emails
• How to predict the responses to your outreach
• How to use Hunter's AI email writing assistant
• How to use AI in Hunter's B2B database
• How to analyze your emails with AI

Building something custom:

• API overview
• API documentation
• Your API keys

AI outbound glossary

Whether you're new to AI or experienced, here's a quick reference to the terms used throughout this guide.

Agent (AI agent): An AI that completes multi-step tasks on its own. It plans the steps, uses tools like Hunter or your browser, checks its own results, and comes back to you when it needs a decision. A chat answers questions; an agent does jobs.

Agentic AI: The shift from AI you prompt line by line to AI that works semi-autonomously. In lead generation, that means handing an agent the whole process, from finding companies through to sending sequences, instead of prompting each step yourself.

AI SDR: An agent doing the job of a sales development rep: researching accounts, finding contacts, writing outreach, and booking meetings. The best-known examples work inbound leads; outbound AI SDRs exist too, and need closer supervision.

AI slop: Generic, low-effort AI output that shows up as recycled phrases in cold email ("I hope this finds you well"), fake personalization, and copy that sounds like everyone else's. Recipients rarely object to AI itself, only to email that reads like it.

API (application programming interface): The way software talks to other software. Hunter's API lets an agent search, verify, and enrich data directly, with no one clicking through the app.

Connector: A pre-built link between your AI and another tool, such as Hunter, your CRM, or Google Drive. Once connected, the AI can read from and act in that tool during a conversation or an agent run.

Context window: An LLM's working memory - everything it can see in the current conversation, including your prompts, your files, and its own replies. Once a conversation outgrows the window, older detail falls away, which is why long lead-gen chats can lose the plot.

Deliverability: Whether your emails reach the inbox rather than the spam folder. Bounces from unverified addresses damage your sender reputation, which is why you verify before you send.

Enrichment: Adding useful data to a bare contact or company record: role, headcount, industry, technologies used, funding, and so on. Agents rely on enriched data to write emails that reference something real about the recipient.

Firmographics: The company equivalent of demographics - industry, size, location, revenue, and structure. These are the filters you'd use to describe your ICP to an agent.

Frontier LLM: The most capable class of AI models available at any given time. Claude, ChatGPT, Gemini, Perplexity, and Grok all qualify today. The frontier moves quickly, so this year's leading model becomes next year's baseline.

Gemini Gems: Google's answer to Claude Skills and ChatGPT GPTs which is a saved version of Gemini with your own instructions and files attached, so it behaves the same way every time you run a task. Useful for repeatable outbound jobs like list checks or email reviews.

GTM (go-to-market): Everything involved in getting your product to customers: positioning, marketing, sales, and pricing. "AI in GTM" can cover any of it; this guide focuses on the lead generation slice.

Guardrails: The limits you set on what an agent can do without you. For example: verify every address before saving it, never send an email without approval, or cap daily sending volume. Good guardrails are what make semi-autonomous outreach safe.

Hallucination: When an LLM states something false with complete confidence, including invented email addresses and contact details. This is why lead generation needs a real data source behind the AI: the model does the reasoning while a database like Hunter's supplies the facts.

Human-in-the-loop: A setup where the agent does the work and a person approves the moments that matter, usually list quality and the final send. It's the practical middle ground between manual prospecting and full autopilot.

ICP (ideal customer profile): A definition of the companies most likely to buy from you, usually written in firmographics plus context like growth stage or tech stack. The sharper your ICP, the better an agent can prospect on your behalf.

Intent signals (buying signals): Evidence that a company may be ready to buy: hiring for relevant roles, raising funding, or recently incorporating. Hunter's Signals tracks these so you can decide who to contact first.

LLM (large language model): The type of AI model behind Claude, ChatGPT, and the rest. Trained on enormous amounts of text to understand and generate language, which is what lets you run lead generation through plain conversation.

.md file (Markdown file): Is a file that you can upload to your AI assistant to give direct, explicit directions (and expectations) of how you want an LLM to perform a task.

MCP (Model Context Protocol): An open standard that lets an AI connect to outside tools and data. Installing Hunter's MCP gives your AI direct access to Hunter's search, verification, and enrichment, whether you're chatting or an agent is running on its own.

Prompt: The instruction you give an AI. A good lead-gen prompt covers the role you want it to play, the goal, the context to use, and the task itself. Vague prompt in, generic leads out.

RAG (retrieval-augmented generation): A technique where the AI looks information up from an approved source before answering, rather than relying on memory. It reduces hallucinations but takes technical setup, which is why most founders get a similar benefit by connecting a verified data source like Hunter instead.

Skills (Claude) / GPTs (ChatGPT): Saved, reusable instructions your AI follows every time it runs a certain task. One example from this guide: a skill that checks every drafted cold email against a list of AI cliches before you ever see it.

Token: The unit LLMs use to process text, roughly three-quarters of a word. Usage and pricing are measured in tokens, and every conversation has a token budget tied to the context window.

Training data / knowledge cutoff: The text a model learned from, which stops at a fixed date. Training data doesn't contain current, verified contact details, and it never will. That gap is exactly what live data connections exist to fill.

Verification (email verification): Checking that an email address exists and accepts mail before you send to it. Statuses like valid, accept-all, and undeliverable tell you which contacts are safe to reach.

Perplexity is good at one thing prospecting tools are not: it reads the open web as it is today and shows you where each claim came from. Ask it what happened this quarter and it answers with a cited list, not a snapshot from a database that was refreshed six months ago.

Even compared to other AI assistants like Claude or ChatGPT, it "likes" accessing the web with an extended suite of tools to find information for you.

What it cannot do is what Hunter does—surface verified contact information and send outreach sequences optimized for deliverabity. It knows a startup raised money last month, but it does not know the name of the head of growth or whether their email is safe to send to. Hunter fills that gap. Connect the two and a fresh market signal becomes a verified, contactable person, with the sources still attached.

This AI GTM walkthrough covers using Hunter in Perplexity for your prospecting.

One thing to be clear about before you start: connecting Hunter to Perplexity requires a paid Perplexity subscription. Custom connectors are only available on Perplexity's paid plans, Pro or higher. A free Perplexity account cannot add Hunter at all, so if you are on the free plan this workflow is not open to you yet.

Here is how to connect Hunter to Perplexity.

Build a market map from live signals

Static databases don't reflect reality. This is why the idea of data provenance is increasingly important with the use of AI. Hunter helps you find the most accurate, up to date GTM data for your prospecting.

When you want companies that did something in the last few weeks, you need a tool that reads the web now and shows its sources.

That is what this step is for: you describe the signal you care about, and Perplexity returns a market map built from current, cited results rather than a stale export. Because the sources stay attached, you can check any entry before you act on it.

Try: "Find companies that [meet a recent trigger, for example raised a Series A this quarter]. Include sources."

A live market map built from cited web results, not a stale export.

Enrich with ICP fit reasoning

A list of names is not a lead list.

Before you spend a lookup on contacts, it helps to know which of these companies actually fit what you sell, and why.

Perplexity can reason over the same list and add that judgement company by company, so you qualify the list first.

The sources stay visible, so the reasoning is something you can check rather than take on faith.

Try: "For each, add what they do and why they'd be a fit for [what you sell]."

Qualify the list before you spend a lookup, with the sources still attached.

Hand it to Hunter for verified contacts

Now the list is qualified, you need people.

This is the handoff: Perplexity has told you which companies matter, and Hunter finds the people behind each one, with an honest read on whether their email is safe to send.

For each company you get names, roles, and addresses, each with a verification status.

Just as important, Hunter is explicit about what it cannot confirm.

Some contacts come back verified and ready to send, some are flagged as unverified or sitting on an accept-all domain where deliverability cannot be guaranteed, and some companies return nobody at all.

What you need to care about:

• A valid result is a green light
• An accept-all domain is a warning that the server accepts anything, so a hit there is not proof the mailbox exists.
• Unverified means Hunter has a candidate it has not confirmed.
• No data means it found nothing and will not guess.

It's crucial to know exactly which contacts you can send to today and which need more work before you risk your sender reputation.

Try: "Use Hunter to find the [role you want] at each, with verified emails."

Hunter tells you what it cannot confirm, which is exactly why you can trust what it can.

Scale it to a whole category

The same workflow that covers a handful of companies can also be used for mapping a whole market segment in one run.

Instead of stopping at a single signal, you can ask Perplexity to map an entire category and attach a decision-maker to each name in the same request.

It works through the list step by step, using its own research to build the market map and Hunter to find and verify a contact for each company, then returns the result with confidence scores, verification statuses, and sources.

Try: "Who are the top [number] players in [your category]? Then find one decision-maker at each with Hunter."

A whole category researched, staffed and graded in one run, with sources.

Where to go next

Perplexity finds what is happening on the open web and cites it.

Hunter finds the people behind those companies, and it is honest about which ones it cannot confirm.

Together they take you from a fresh signal to a checked shortlist without leaving the chat, and you never send to an address Hunter could not confirm.

If Perplexity is not your assistant of choice, the same connector approach works elsewhere:

• Here is the same workflow with Hunter and ChatGPT
• Here is the same workflow with Hunter and Claude.

For the full picture, see the Hunter and AI assistants hub.

Connect Hunter to Perplexity and run your first market map today.

Frequently asked questions

Do I need a paid Perplexity plan to connect Hunter?

Yes. Custom connectors are only available on Perplexity's paid plans, Pro or higher. A free Perplexity account cannot add Hunter, so you will need a paid subscription before you can set up this workflow.

What do Hunter's verification statuses actually mean?

Valid means the email was confirmed and is safe to send to. Accept-all means the server accepts any address at that domain, so a hit is not proof the mailbox exists. Unverified means Hunter has a candidate it has not confirmed. No data means Hunter found nothing and will not guess. The point is that Hunter tells you what it cannot confirm rather than inventing an address.

Why use Perplexity for the research step instead of Hunter alone?

Perplexity reads the open web as it is today and cites its sources, so it can surface companies based on recent events. Hunter then turns those companies into verified, contactable people. You get current signals and checked contacts in one flow.

Can I save the contacts Perplexity finds straight into Hunter?

Yes. Once the list is built, Perplexity can save the results to a Hunter leads list, so the shortlist moves into Hunter without a manual export.

An AI assistant on its own is quick to reason but has nothing to reason over.

It can talk about your ideal customer in the abstract, but it cannot name the companies that fit, find the people who make decisions there, or tell you whether an email address is safe to send to.

Give it Hunter's data and that changes. The same assistant now works from your real wins, returns named and verified people, and leaves you with something you keep and come back to.

This AI GTM walkthrough covers using Hunter in Claude for your prospecting.

You connect Hunter to Claude as a custom connector, which is a remote MCP connection. It works on the Free, Pro, and Max plans. On the Free plan Claude allows a single custom connector, so Hunter can be the one you add.

If you have not connected Hunter to Claude yet, do that first, then come back and follow along.

Let Claude work out your ICP from your wins

Whether you've defined an ICP or not, you can skip that step and let Claude infer the pattern from customers you have already closed, then act on it in the same message.

Because Claude reasons before it fetches, it works out what your wins have in common, describes that profile back to you so you can sanity-check it, then queries Hunter for companies that match.

The list you get is built from your own closed deals.

Try: "Here are the customers we've closed: [list your domains]. Work out the ICP they share, then use Hunter to find companies that match that pattern."

Claude infers your ideal-customer profile from past wins, then pulls matching companies from Hunter.

Score and rank the whole list

A list of matches is only useful once you know where to start. Because Claude holds the whole list in its context window, it can weigh every company against the profile at once rather than one row at a time, and explain each call.

Ask it to score and rank, and the prioritization comes back with a reason attached to each position, so you can push back on any ranking instead of trusting a score you cannot see into.

Try: "Score all of them against that ICP from 1 to 10 and rank them, with a one-line reason for each score."

Claude scores and ranks every company against the profile, with a reason behind each position.

Build decision-maker lists with emails you trust

A ranked list of companies still is not a list of people you can email.

This is a critical step where you can use Hunter's data in Claude: it turns each company into named decision-makers with verified email addresses and a confidence score.

Even better, Claude will draw your attention to any gaps. When an address fails verification it is flagged or left out rather than passed through, and when Hunter has nobody indexed at a company Claude says so instead of inventing a name.

You end up with a list you can send to and where you need to research further.

Try: "For the top companies, use Hunter to find the decision-makers in [the roles you sell to] with their verified emails."

Hunter turns each company into named decision-makers with verified emails, and flags the ones it cannot confirm.

A hook that fits every contact

You now have people worth contacting, and generic outreach wastes that.

Because Claude knows each contact's role and what their company does, it can draft a first line that fits the person rather than a mail-merge template.

Then, instead of leaving you with a wall of text, you can ask it to consolidate everything into something you can actually work from.

Try: "For each contact, write a one-line outreach hook based on their role and what their company does. Then turn this into an interactive account-scoring sheet I can reopen and reuse."

Claude writes a hook that fits each contact, then starts pulling the results into a tool.

Your reusable account-scoring sheet

Here an agent does something a plain chat window cannot.

Instead of a reply you copy out and lose, Claude builds an interactive sheet: every account with its score, its contacts and their verified emails, and editable fields for priority and status.

You can sort it, export it, and reopen it later with your edits still in place.

The workflow becomes a tool you turn into a process for easier repetition.

The finished artifact: scores, verified contacts and editable status per account, in a board you can reopen and reuse.

Where to go from here

That is one prompt turning into a scored, contactable, reusable pipeline.

Hunter brought the companies, the verified emails, and a straight answer about the gaps, and Claude helped you turn this into a process

Ready to try it? Connect Hunter to Claude and run the first prompt against your own wins.

Prefer a different assistant?

• Run the workflow using the Hunter ChatGPT app
• Run the workflow using Hunter in Perplexity.

For the full picture, see our Hunter and AI assistants hub.

Frequently asked questions

Which Claude plans can connect to Hunter?

The custom connector works on the Free, Pro, and Max plans. On the Free plan Claude allows a single custom connector, so you can make Hunter the one you add.

Do I need to write an ICP brief before I start?

No. Claude can infer the shared profile from a handful of customers you have already won, then use Hunter to find companies that match it.

How do I know the email addresses are safe to send to?

Hunter verifies each address and returns a status and confidence score, and it flags or drops the ones it cannot confirm rather than passing bad data through.

Is the account-scoring sheet a one-off, or can I reuse it?

It is a reusable artifact. You can sort it, export it, edit the priority and status of each account, and reopen it later with your edits still in place.

Without access to B2B data, ChatGPT isn't very useful for email outreach.

Ask it to find companies like your best customer and it will guess from memory: no counts, no contacts, nothing you could email today.

Best case scenario? It will use web search to look up individual companies or go off listicles and other easily available content.

But, the moment it can reach Hunter's data, that changes.

With access and your guidance, ChatGPT can do everything you can do inside Hunter and more.

This AI GTM walkthrough covers using Hunter in ChatGPT for your prospecting, covering:

• How to go from one company you already sell to well to a written cold opener, without leaving the chat window.
• Why you only need a free ChatGPT plan and no CRM
• How to get the Hunter ChatGPT app
• How to use a ChatGPT conversation to use plain language into real leads.

You can keep shaping any result just by talking to it – whether the list is too broad, you need to find leads in a region or department – ask, and you'll get the answer.

If you want to know how to connect Hunter to ChatGPT first, see how to connect Hunter to ChatGPT.

Start from a customer you already win with

The best prospecting starts from find companies you know (and want to) win.

You may not have the luxury of having clear wins to leverage, but if you do, that's where you should start.

Give ChatGPT one domain and ask it to find companies that look the same.

Try: "Here's a company we sell to well: [domain]. Find 10 similar companies I should target, with a reason each fits. Show them in a table."

You bring up the Hunter app menu by typing @hunter, and Hunter's Discover does the work.

It returns an interactive widget and lists them with the number of email addresses on file and the industry.

One domain turns into a prioritized list of leads to target.

One customer domain becomes a ranked list of 366 lookalike companies, each with an email-address count.

Narrow target companies in a sentence

You now have companies to review.

In a conventional user interface, the next step is clicking through filters.

Not here. In ChatGPT, just say what you want.

Try: "Narrow that list to companies in Europe."

A single sentence trims the market to European companies, each with a one-line reason it belongs.

Find companies with buying signals

A company can fit your profile and still not be worth prospecting right now.

One that just raised money or is hiring fast is a better first call than one sitting still.

Ask the agent to go and check.

Try: "Which of these recently raised funding or are hiring aggressively? Look for such news and hiring signals and flag them."

It researches the open web and comes back with a table. It closes with a recommended outbound order and a segmentation. The agent has done the reading and prioritized for you.

The agent checks the open web for funding and hiring signals, then ranks who to contact first.

Get named contacts with verified emails

Up to here you have companies and reasons, but you cannot email a company.

You need people, with real addresses, that are accurate. Hunter shines here.

Try: "Use the Domain Search on those companies, then filter the results to marketing."

Domain Search returns named marketing contacts with addresses and a verification status on each one.

Draft the first touch

You have a prioritized company, a named contact and a verified email.

The last step is the message, and the agent already knows enough to write it.

Try: "Write a 3-sentence cold opener for contact #1, using their role and what the company does."

It produces an editable text block with a three-sentence opener addressed to Matthias at Adyen, written for a VP of Revenue Marketing at a payments infrastructure company, and offers to make it more technical, more executive or more personalized.

You went from one customer you like to a first email in one chat.

A three-sentence opener, written for the contact's role and company, ready to edit and send.

Try it on the free plan

You do not need a CRM, a paid seat or a setup call to run this.

It works on the free ChatGPT plan with the Hunter app, and all of it happened in one conversation, from the first lookalike search to the finished first email.

If you would rather do this in a different assistant, the same idea works elsewhere:

• Run it in Claude
• Run it in Perplexity, and
• Get the full picture in Hunter with AI assistants.

Frequently asked questions

Do I need a paid ChatGPT plan to use Hunter this way?

No. Every step here runs on the free ChatGPT plan, and Hunter is available as an app inside ChatGPT.

Do I need a CRM or any setup first?

No. You start from a single company domain in the chat window, and there is nothing to install or import.

Are the email addresses verified?

Yes. Even inside ChatGPT, Domain Search returns a Hunter verification status on each contact, so you can see which addresses are marked Valid before you send.

No single data provider will find everything. Run a 10,000-contact list through any email finder, ours included, and a chunk of it comes back empty.

Nobody likes that.

Waterfall enrichment is an approach to data enrichment that aims to fix it: when one provider has no data, ask the next one, and keep going until something comes back. And if building a waterfall and doing it by hand sounds burdensome, a wave of tools now sells waterfalls as the product itself.

Hunter sits inside many of those waterfalls. Clay and other platforms query our data as one of their steps, so we watch this technique work from the inside. In fact, Hunter proudly sits at the helm of Clay's waterfalls, and we've received honors as a top provider for accept-all verification and work email finding.

Waterfall does meet its goal: stacking providers will fill more rows than any single one of them. But coverage is the only thing a waterfall guarantees.

The trade-offs (accuracy, consistency, and price) only become apparent once you've used one for a while.

If you're wondering whether you should even care about this topic, here's a TL;DR:

• Waterfall enrichment means asking multiple data providers for the same data point, one after another, until one of them returns it. In practice, that mostly means finding and verifying email addresses.
• Its one guaranteed benefit is coverage: on a large list, several providers together will fill more rows than any single one.
• The costs: more guessed addresses, verification statuses that stop meaning one thing, multiplied bills, and murky data sources.
• It's the right tool for agencies running outreach at massive scale, with the volume to justify the overhead and the ops capacity to manage several vendors.
• For lean businesses and smaller data needs, quality beats quantity: a single vendor like Hunter will get you consistently better outreach results with none of the overhead.

This guide is waterfall enrichment 101, and will help you decide if you're ready to adopt the process.

What is waterfall enrichment?

Waterfall enrichment means using multiple GTM data providers on the same data point, one after another, until one of them returns a result. Provider A goes first. If it finds nothing, the record moves to provider B, then C, and so on down the sequence. The first hit ends the run.

The point of using the waterfall enrichment approach is coverage. On a large list, no provider fills every row, so the waterfall exists to recover the records your first pick couldn't.

To clarify, waterfall enrichment is a technique, not a product.

You can build one yourself with 2-3 API keys, run one inside Clay, or buy it packaged from tools like FullEnrich and BetterContact that sell the waterfall as the product.

What waterfall enrichment is used for

In theory, you can "waterfall" any data point: phone numbers, job titles, headcount, and/or tech stack.

In the context of Hunter, enrichment covers contact, company, and technology data. It's verified, accurate data, which makes it the right starting point.

Anything beyond that (intent data, funding rounds, and job postings) can run through the same process.

But when someone says waterfall enrichment, it's a safe bet they mean some combination of the three jobs above. That's the scope of this article too.

Finding contact information

You have a name and a company domain, and you want an email address (or a phone number). The record runs through finders until one returns an address. This is the use case most waterfall tools are built around, and the one their coverage claims refer to.

Verifying contact information

You already have an email address, and you want to know whether it's safe to send to. The address runs through verifiers until one returns a clear status.

Waterfall process or not, don't skip this step, especially before reusing an old list in a campaign.

Enriching leads and companies

You already have the contact, an email address or a company domain, and you want the data around it: name, role, location, company size, and industry.

This is the job of enrichment APIs, including Hunter's. Give them an email address or a domain, and they return the person or company profile behind it.

Different providers use different attributes for the same record, so profiles with gaps can cascade through several enrichers the same way emails cascade through finders.

How a waterfall works, step by step

A waterfall is like trying to book an urgent doctor's appointment and you're calling multiple offices, starting with the one you want the most, to find one that can see you today.

When you're trying to find an email address, this is the process:

1. You decide the order of providers: A, then B, then C.
2. Every record goes to provider A first.
3. If A returns an email address, that record is done. It never reaches the other providers.
4. If A returns nothing, the record moves to B, then C, until something comes back or the queue runs out.
5. Optionally, every found address goes through a verifier at the end, no matter which provider found it.

Two details in that flow decide most of the economics.

1) Provider order

The cheapest provider goes first in the hope that it resolves most of the list at the lowest cost, and the expensive providers only ever see the leftovers. It's the sensible default with a significant drawback we'll discuss in a moment.

2) How you pay

Some providers charge only when they return a result, others charge per lookup no matter what comes back, and packaged waterfall tools usually charge a flat credit per completed contact regardless of which provider did the work.

Why you'd use waterfall: coverage

Waterfall enrichment promises better coverage.

That’s a natural outcome when you combine different GTM data providers.

For example, some providers have strengths in publicly sourced, verified data across companies, contacts, and technologies.

Others give fantastic coverage of European data, while others succeed with North America.

Meanwhile, some vendors give GTM data specialized on smaller businesses, enterprises, emails addresses, and even phone numbers.

The point being, a waterfall of different GTM data providers can give you a more complete coverage for your leads.

The waterfall enrichment approach lends itself well to needs of outreach agencies. They have the time, resources, and scale, to test things, make tweaks, and maintain the waterfall over time.

The trade-offs: accuracy, consistency, and price

The extra rows come at a cost:

Accuracy: a returned email isn't a correct email

A waterfall stops at the first answer it gets. That's hiding a real weakness - it's not checking whether the data is right.

That matters because providers need different amounts of evidence before they serve you an address.

Some generate the likely pattern (first.last@company.com), check that the server doesn't object, and return it as valid.

Others are careful differentiating between valid and accept-all, and run multiple extra checks on top of the SMTP handshake.

Inside a waterfall, the forgiving providers naturally claim a bigger share of your list. Every record they "find" never reaches the stricter tools behind them. The more eager the finder, the more guessed addresses end up in your outreach.

You'll meet them again as bounces.

Consistency: providers don't speak the same language

Every provider has its own statuses and its own logic behind them. Accept-all domains are the classic case.

Their mail servers accept messages to any address, real or not, so no verifier can fully confirm the mailbox exists.

One provider can name these addresses as "risky". Another will call them "valid" with a footnote. A third can fail to make a comment on the status. It's the same email address, but quickly the efficacy of the email address is forgotten.

Merge those outputs into one CRM column and "verified" doesn't mean a thing.

Two rows have the same status but passed different tests run by different tools.

This can be mitigated, but it's a ton of logic you'll need to build into your process.

Price: increases with more coverage

You're now paying several vendors instead of one, through a mix of credits and subscriptions.

Cheapest-first ordering keeps the cost per found email down, but the hard records still cascade through every paid lookup.

Packaged waterfall tools simplify this into one flat credit per contact, and they price that convenience in.

The measurement trap: you stop knowing which provider is good

In a waterfall, the first provider sees the whole list and resolves the easiest step: known companies, standard email patterns, and contacts every database has.

By design, providers further down only ever see what the ones before them missed.

But it means position in the queue decides the stats. Provider A found, say, 65% of what it was asked and provider C found 12%. A went first, so it got the records anyone could find. C got the leftovers.

Swap their places and C might find 72%, and suddenly look significantly more useful.

The same problem applies to your CRM data.

A bounce doesn't tell you which provider produced the address. Unless you record the source of every found email and check campaign results per source, you'll never learn which tool is worth re-using, and which ones should be dropped.

If you don't track it, the impression that sticks is "provider A found the most". It looks like the cheapest and best tool in the chain, but you can't actually know that without proper analysis.

The compliance problem: a waterfall is as compliant as its weakest provider

If you email people in the EU, someone may eventually ask where you got their address. Under GDPR, you're expected to have an answer: what the source was and on what basis you're processing the data.

With one provider, that's one answer. You know their methodology, you've read their DPA, and you can point to the source when asked.

With a waterfall, you have as many answers as providers, and they're not equally good. Some document where each data point comes from (Hunter tells you how every email address was found). Others give you an address and nothing else. You don't know whether it was crawled, licensed, scraped, or guessed.

Data provenance doesn't average out. If a third of your list came from a provider that can't name its sources, that's the third that's unaccounted for.

The careful providers in your waterfall chain don't compensate for the opaque ones, because compliance questions come one record at a time.

Log where each address came from, and you can at least answer the question.

When waterfall enrichment makes sense (and when it doesn't)

The decision comes down to what a missing row costs you vs. a bad row.

A waterfall is worth it when:

• Coverage is the constraint. You've exhausted your primary GTM data provider, and the records it missed are worth it, say, in a niche market where every account matters.
• Your list is big enough that a few extra percentage points of coverage mean hundreds of extra contacts.
• You have the money and time to handle it. Several vendors mean several bills, and somebody has to own the setup, watch the credit balances, and run it.
• You have the ops capacity to do it right: normalize statuses across providers, track sources per record, and watch per-provider quality.
• You treat it as backfill. One trusted provider does the bulk of the work, and the waterfall mops up what's left.

Skip it, or keep it short, when:

• A bounce can hurt your sending domain.
• You sell into compliance-sensitive markets and can't afford fuzzy provenance.
• Your list is small (i.e., <1,000 contacts), and you can fill the gaps by hand faster than you can wire up three providers.
• A single provider already covers your segment well. Coverage problems you don't have don't need solving.

If you run a waterfall, run it well

None of the problems above kill the idea of using a waterfall.

If you're going to run a waterfall:

1. Start with the strictest finder, even if it costs more per lookup, because it means pattern guesses only enter your list when nothing better exists. It does cost more per contact, but it will reduce your bounce rates.
2. Verify everything with the same tool at the end. Whichever provider found the address, one verifier of your choice gets the final word. That way "valid" means exactly one thing in your CRM.
3. Decide your accept-all policy once. Whether you send to accept-all addresses is your call to make upfront, not something to inherit from whichever provider happened to label the row.
4. Record the source of every address if possible. It's how you evaluate providers, and it's your answer when someone asks where their data came from.
5. Cap the chain at 2-3 providers. Many tools license data from the same upstream sources, so the fourth provider in your queue often searches a near-copy of a database you've already tried. Each addition finds less and costs the same to maintain.
6. Judge GTM data providers by their bounce rates. Bounce rate reflects whether the addresses were actually right, while find rate is whatever queue position makes it.

Frequently asked questions

What is waterfall enrichment?

Waterfall enrichment means using multiple data providers on the same data point, one after another, until one returns a result. It's mostly used to find and verify email addresses. The goal is coverage: on a large list, several providers together fill more rows than any single one.

How is waterfall enrichment different from regular data enrichment?

Regular enrichment asks one provider and accepts the gaps.

Waterfall means more sources, which means higher coverage, but comes with trade-offs in accuracy, consistency, and cost.

Does waterfall enrichment improve accuracy?

No, it improves coverage. A waterfall stops at the first answer it gets, whether or not it's right, and providers with looser standards fill more of the list. If accuracy matters, run every found address through the same verifier at the end, no matter which provider found it.

Is waterfall enrichment GDPR compliant?

It can be, but it's only as compliant as its least transparent provider. GDPR expects you to be able to say where personal data came from. If one tool in your chain can't name its sources, you can't answer for the records it produced. Check each provider's methodology before adding it.

Which tools offer waterfall enrichment?

Clay, FullEnrich, and BetterContact are the best-known options, and platforms like Apollo have added waterfall features of their own. You can also build one with 2-3 provider APIs. The tooling matters less than the setup: order, verification, and source tracking decide what you get out of it.

Is waterfall enrichment worth it?

It depends on what a missing row costs you versus what a bad row costs you. It's worth it on large lists in coverage-constrained markets, with the budget and ops time to run it properly. It's not worth it on small lists, or when a bounce hurts more than a gap.

Making the call

A waterfall improves your coverage, but it will affect the accuracy, consistency, legal compliance, and costs of lead generation.

Sometimes that's a good trade.

If every account in your market matters, take the extra rows and manage the rest with the checklist above.

Wherever Hunter fits in your setup, as your only provider or the strict first step in a chain, you'll know how every address was found and what its status means. That part shouldn't depend on which tool answered first.

Any waterfall can fill the rows, but with Hunter as a core data source you know you can trust the results.

Meet NawRath

NawRath is a boutique compliance consulting firm based in Chile, providing risk management, internal audit, and regulatory compliance services to enterprises across sectors including mining, healthcare, and aviation. 

Industry

Consulting

Challenge

Scaling a single person sales team

Tools used

Hunter's Email Finder, Email Verifier, and Sequences

Headquarters

Santiago, Chile

With a team of eight specialists - all with backgrounds in business administration and internal audit - the firm advises companies on meeting an increasingly complex and demanding regulatory environment in Chile.

As a small, specialist firm, sales fall directly to Joaquín Nawrath. With no dedicated sales team, he's both the General Manager and the one doing prospecting, outreach, and client development - all while managing the delivery side. 

Getting in front of the right decision-makers - CEOs, legal counsel, controllers - and converting those conversations into meetings is the entire commercial engine of the business.

The challenge

Before Hunter, Joaquín ran his outreach the hard way: identify contacts on LinkedIn Premium, manually copy each email address, open Gmail, and send one message at a time. 

The approach worked - he could generate meetings and close clients - but it was painfully slow.

In a full week of email activity, he might send 20 to 30 messages, generating one or two meeting requests.

"Before Hunter, I was doing the same, but it was way slower... Now we achieve better results, faster."

Joaquín Nawrath, General Manager, NawRath

The bottleneck wasn't quality - it was volume. 

Reaching CEOs and legal counsel at mining companies, manufacturing firms, and airlines required significant manual effort for every single contact. 

At the pace he could sustain solo, the business was leaving a large portion of its potential pipeline untouched.

The solution

Joaquín uses Hunter to run his full outbound process - from list building to sending email sequences - within the two hours per day he allocates to sales.

1. He starts on LinkedIn SalesNavigator, identifying CEOs of mining companies, legal counsel at manufacturing firms, controllers at regulated enterprises, then uses Hunter to find their email addresses
2. He builds sequences of 200-300 contacts, writing tailored messaging for each segment within: strategic-level copy for CEOs focused on competitive positioning, operational-level copy for legal counsel focused on pain relief, and technical-level copy for internal audit professionals who need specifics over persuasion.
3. Sequences run entirely within Hunter. The goal of every sequence is simple: secure a meeting. Nearly every company that takes a meeting then receives a commercial proposal - making the meeting-booking rate his primary conversion metric.
4. Hunter also serves as a staging area for contact data, keeping his CRM clean. He maintains lists in Hunter and only moves contacts to his CRM when they show genuine intent.

He compares the approach to Google Ads, which he also runs:

"The precision of the lead in Hunter is stronger than Google Ads. With Hunter, I go straight to the decision maker. With Google Ads, there is less qualification, meaning that when I do the meeting, sometimes it's people that have no budget or authority to act.”

Joaquín Nawrath, General Manager, NawRath

The results

In the year before Hunter, Joaquín reached 20 to 30 companies through his outreach. Last year, with Hunter, that number was 300.

"Last year I reached 300 companies in one year. If I compare this with before Hunter, the number is no more than 20 or 30 companies in a year." 

Joaquín Nawrath, General Manager, NawRath

He now consistently generates three to four qualified meetings - meetings that almost always convert into commercial proposals. And the quality of those leads is higher than any other channel he runs. 

He attributes 40-50% percent of his total commercial reach to Hunter.

For a General Manager-as-sales-team, the platform has effectively replaced the role of a dedicated marketing hire - at a fraction of the cost.

The takeaway

For Joaquín, Hunter solved the core constraint of running sales as a one-person operation: time. 

The work of identifying and reaching the right senior decision-makers at regulated enterprises is increasingly hard. 

Hunter didn't change what that work requires - it made it possible to do at scale, without adding headcount.

As Joaquín puts it himself:

"Hunter is like having a marketing assistant, but incredibly affordable. Even if I spend two, three hours a day in Hunter, I’m saving the expense of one person."

Meet AeroChat

AeroChat is an AI-first customer communication platform that helps SMB to mid-market Shopify merchants automate and scale their support.

Industry

SaaS

Challenge

Verifying and enriching lead data

Tools used

Hunter's Domain Search and Email Verifier

Headquarters

Singapore

By using a proprietary contextual engine to better detect customer intent, they resolve between 87% and 94% of incoming inquiries, helping merchants turn conversations into conversions to sell faster and sell more

The challenge

AeroChat had already built a highly successful inbound growth engine heavily reliant on SEO and AI search visibility. However, to reach their ambitious growth targets and sustain their momentum, they knew inbound alone wasn't enough. They needed to unlock outbound.

The problem? Their early outbound strategy was, as founder Alva Chew described it, "hazardous". Without a centralized system, they relied on scattered networking events, exhibitions, and "half-hearted" LinkedIn direct messages.

They knew exactly who their ideal buyers were - Shopify merchants - but whenever they pulled target domains from third-party platforms, the email data they got back was incomplete, unverifiable, or completely fake. 

Sometimes merchants used personal email addresses, making it impossible to verify whether the contact information was legitimate. They needed a way to execute outreach systematically at scale, but bad data was blocking them at the starting line.

The solution

Realizing that reliable data was the missing link, AeroChat shifted to a highly targeted, systematic approach. Hunter became the engine that powered this transition.

Today, their outbound workflow runs like a well-oiled machine:

1. Sourcing: They find domain names of verified Shopify websites from third-party platforms.
2. Identifying: They add those domains directly into Hunter, using Domain Search to crawl and identify the relevant email addresses associated with those target accounts.
3. Verifying: They run the newly built list through Hunter's Email Verifier to guarantee every email is legitimate and active.
4. Outreach: The clean list is handed over to their marketing specialist to run targeted outreach.

The results

Hunter bridged the critical gap between identifying target domains and actually reaching the right inbox. With clean data, AeroChat's outbound channel is thriving.

"Hunter plays a very critical aspect of our entire process. Without Hunter’s verified emails, there’s no way that we can run this outreach process." 

Alva Chew, Founder, Aerochat

By ensuring they are reaching valid inboxes, they are seeing incredible engagement metrics:

• Massive engagement: “From our email sequences, we’re enjoying 60% open rates and anywhere up to a 40% click through rate.”
• Brand lift: AeroChat tracks a general lift in branded search terms, reflecting the compound success of their outbound and inbound activities working together.

The takeaway

For AeroChat, finding a simple, reliable verification solution didn't just clean their data - it unlocked a completely new growth channel for the business.

As Alva says himself:

"Hunter solves a very simple yet critical step in our outbound process."

Meet Goodjuju

Goodjuju is a local SEO and marketing agency that works with property managers and real estate professionals across the US.

Industry

Marketing

Challenge

Scaling outreach to verified leads

Tools used

Hunter's Discover and Email Verifier

Headquarters

Lehi, UT, USA

Founded by Landon Murie, a former property manager himself, Goodjuju helps clients climb Google's local search rankings and generate more leads through a "hyper-local SEO" approach: one that goes beyond keywords on a webpage to optimize Google Business profiles, build authoritative backlinks, and engineer visibility in the map pack results that drive the most clicks.

What sets Goodjuju apart is Landon's background. 

Having managed properties in Utah, he knows the business his clients are in: the pressure of filling vacancies, the confusion of dealing with marketing agencies that need to be taught the basics, and the frustration of paying for services that don't deliver. 

That insider knowledge shapes everything: the way Goodjuju sells, how it messages outreach, and why their 13-person team has generated seven digits of annual revenue.

The challenge

When Landon started Goodjuju, he had no outbound tool, no CRM, and no process - just a spreadsheet and a lot of determination.

He would search Google for property managers who looked like they needed help, navigate to their websites to find the owner's name, guess at an email address, and send each message manually from Gmail, one at a time.

"I had a Google sheet and I would try and find their name, the email, and then I wasn't even using a tool. Just putting them in Gmail and sending them off, one by one."

Landon Murie, Founder, Goodjuju

That approach built Goodjuju's first clients but couldn't scale. 

With a total addressable market of fewer than 8,000 property management companies in the US, every lead mattered - and wasting them on bad data or mismatched targeting meant burning through a finite pool of potential customers. 

As the team grew and the business became more sophisticated, Landon needed a faster, cleaner way to build targeted prospect lists and verify that the people he was reaching were real, reachable, and right.

The solution

Goodjuju now uses Hunter as the engine of its outbound prospecting - from finding companies to sending sequences. 

Because the target market is so defined, precision matters more than volume, and Hunter's filtering capabilities in Discover give Landon exactly the control he needs:

1. Landon uses the real estate category as a base, refining it with negative keywords until the list is clean. He filters by company size (4–20 employees) and revenue (under $1.5M) to focus on owner-operated firms where the decision-maker is hands-on, invested, and reachable, not buried under layers of management.
2. Every contact is verified before outreach. If a lead is missing a first name or a verified email, it gets deleted. Landon doesn't send any emails to leads he can't verify.
3. Sequences run entirely within Hunter. The outreach is deliberately short and pattern-interrupting: a subject line that honestly names what the email is about (gaps holding their local rankings back), followed by a brief message offering a free personalized Loom video analysis - no pitch, no pricing, just an offer to show them what they're missing.

"I see the list getting cleaner and cleaner and cleaner…I can tell the targeting is working when I see existing Goodjuju clients appearing in the results. In one recent session, I built a list of 2,700 contacts in a short window of time.”

Landon Murie, Founder, Goodjuju

That last step - the Loom video - is where Goodjuju's process diverges from standard email playbooks.

Landon records a customized 8-to-12 minute walkthrough of a prospect's local SEO gaps, mentions a comparable client's results, and closes with a simple: "if you want help, let us know." No Calendly, no pressure, no sell.

The results

The combination of targeted outreach with Hunter and the Loom video approach has become Goodjuju's most powerful growth lever. 

When Landon tracks what happens after a video is sent, the data is telling: when a prospect watches the entire Loom, his close rate is 62%. 

That means every qualified lead who sits through the full analysis converts to a client nearly two thirds of the time - and Goodjuju's qualified lead value is $8,700, with a lifetime client value of $87,000.

"We've gone from nothing to almost two million a year revenue... I don't talk to most of the people." 

Landon Murie, Founder, Goodjuju

Email outreach is the primary driver of that growth. When Good Juju is actively running campaigns, it outperforms every other channel, paid ads included. 

The targeting is precise enough that despite prospecting a small niche, the quality of leads it generates is consistently higher than anything coming from Facebook or Google Ads, at a fraction of the cost.

As Landon puts it:

"It's hard to put a number on it. It's insanely valuable."

The takeaway

For Goodjuju, Hunter solved the core problem that limited the business in its early years: not finding property managers to target, but finding the right ones, fast, with verified data. 

In a niche where there are fewer than 8,000 potential clients in the entire US, burning contacts on bad addresses or mismatched companies is not an option. 

Hunter's filtering, verification, and sequencing tools turned a manual, error-prone process into a repeatable outbound system that has driven the agency's growth to nearly $2M in revenue.

As Landon puts it himself:

"When you do outbound email right, it's unbeatable. And Hunter gives you everything you need to do it the right way."

Meet Paperstac

Founded in 2015, Paperstac brings the process of buying and selling mortgage debt into the modern era. They replaced spreadsheets and emails with a digital platform featuring online escrow and audits, making it safe for anyone to trade debt.

Industry

Real Estate

Challenge

Finding verified leads for email and ads

Tools used

Hunter's Domain Search, Email Finder, and Email Verifier

Headquarters

Orlando, FL, USA

After building a strong retail user base of nearly 40,000 people, they’re now targeting the enterprise market. 

Their new target buyers manage massive debt trades, including companies with $500 million in assets under management or billion-dollar portfolios.

The challenge

As Paperstac expanded into the enterprise space, they had plenty of unknowns to address.

As Brett Burky, Paperstac’s Marketing Director describes, buyers were "unknown unknowns,” because larger organizations don’t typically respond to basic sales calls or generic Facebook ad blasts. 

Additionally, the industry's average age is 50 and older, which means email remains the most effective way to generate leads.

Paperstac needed a way to map out this exclusive market and confidently secure the right contact information to get their foot in the door. 

Their previous approach, based on guesswork and patching tools together, wouldn’t scale:

• They would search for a partner or servicer and try to guess their email structure, like "first initial dot last name".
• They would then place multiple variations of the guessed email address in the BCC field, hoping one would deliver.

As they shifted to enterprise targets, this method was no longer a viable way to build trust with billion-dollar companies.

The solution

With the need to adapt, Brett developed a system to understand Paperstac’s market in a way that would enable multi-channel outbound to create a halo effect with their enterprise audience. Brett now identifies great-fit leads using this outbound framework:

1. Mapping the network: Brett uses a network mapping tool to export the contacts of established players attending industry conferences.
2. Scoring the leads: He cross-references these lists to find individuals who appear frequently across multiple networks, identifying the true consensus players in the market.
3. Finding contact info: Brett then uses Hunter to look up the top-scoring companies and verify their email structures.
4. The "halo effect": Before sending a single email, he uploads the verified list from Hunter to create custom and lookalike audiences on Facebook and Google, building brand awareness.
5. Personalized outreach: Once the prospect is warmed up by ads, Brett uses verified email addresses to send tailored offers with specific mortgage notes that perfectly match the buyer's criteria.

The results

The strategic value Hunter provides Paperstac is immense. 

By identifying the exact web of influence in their market, Brett transformed his outreach from cold guessing to highly targeted, omnipresent campaigns.

"Hunter is just magic." 

Brett Burky, Marketing Director, Paperstac

Hunter has been a constant subscription for the team since around 2017.

It sits right alongside SalesNavigator in Brett's browser as a vital tool for navigating a demographic that relies heavily on email communication

The takeaway

For Brett, Hunter isn't just an add-on; it's a foundational element of his daily workflow. 

Hunter turns the complex web of enterprise networking into accessible, verifiable contact data, enabling Paperstac to confidently scale their innovative platform to the biggest players in the mortgage debt industry

As Brett says himself:

"I will never not have it. It's one of my core tools. Sometimes you'll have something you might use, and you forget about it, but not Hunter."

When your send list is full of valid or accept-all email addresses, and bounce rates are below 3%, but you’re still landing in spam folders, what do you do?

Run this play to resolve your deliverability issues, step by step.

Why this matters

Spam placement is hard to diagnose because nothing tells you it happened. 

A bounce sends you a message, but an email finding its way into junk doesn’t. 

It’s critical to identify the issues, because placement failures feed themselves. 

Every email filtered to spam lowers your sender reputation, which sends more of the next campaign to spam.

Here’s how to make sense of your email deliverability headaches in Hunter.

Your plan

• What you'll achieve: Identify a specific cause for your placement drop, a corrected sending setup, and a checklist to help prevent a repeat occurrence.
• Use Hunter tools to diagnose and overcome:
• • Free tools: Email Deliverability Checker, SPF Record Generator, DMARC Record Generator, and Email Warm-up Calculator. All four run in your browser with no account and no limits.
  • Paid features: Global Health and the Deliverability Guard need a paid All-in-one Outreach plan. Inbox Protection is a further add-on, billed monthly per email account. Steps 2, 3, and 5 work without either.
• Learn more about deliverability: With our free email deliverability 101 guide.
• Time to complete: About 20 minutes for the checks, plus 30 days if you warm an account back up
• When to run this play: A bounce rate under 3%.
• When to run another play: A bounce rate above 3%, run this playbook. Remove Invalid and Disposable addresses and treat Unknown with the same caution. Accept-All can stay at a Confidence Score of 85% or higher, in a separate send list.

Step 1: Confirm if there’s a placement issue

Use the guide below to know where you to place your next step:

• Your open rate fell and nothing changed in your subject line or list: This is a deliverability issue – move forward with this play.
• Opens unchanged by reply rates dropped: Your emails are reaching the inbox, which means that your challenge lies in the email body or offer used. You need to try different offers. 
• Your bounce rate went above 3%: Run the bounce rate playbook instead.

Learn more about email deliverability with our webinar

Step 2: Check your domain records

Authentication problems with the domain that your email address is associated with are the quickest (and cheapest) fix to try first.

Visit Run your sending domain through the free Email Deliverability Checker. 

This checker is free, and will enable you to scan critical parts of your domain - SPF, DKIM, DMARC, and MX - plus blacklist status.

No Hunter account is needed. If you pass every check here, move to Step 4. 

If you see errors, turn your attention to addressing each in order, as we will below.

Step 3: Rebuild the failing records

With Hunter, you can generate free records for both your SPF and DMARC.

Each gives you a plain-English explanation of what these are, why you need to update them, and walk through what to do with the newly-generated copy-ready TXT records.

SPF record missing or broken

The SPF Record Generator lets you tick the services you send from and adds the right include for each. 

It also warns you before you cross the 10-lookup limit that makes SPF fail. 

DMARC record missing

The DMARC Record Generator builds your policy and reporting tags. 

Start on monitor-only so nothing gets blocked while you read the reports, then move to quarantine and reject.

Publish the record at your DNS provider.

Once it propagates, re-run the Deliverability Checker to confirm it's live.

Learn how to fix these issues with this guide

Step 4: Isolate continually failing accounts

If your records pass your check following step 3, and you're still in spam, the problem is account-level. 

If you are on a paid Hunter plan, you can connect your email accounts to Hunter and use the Email Accounts Center to re-diagnose.

If you have yet to connect your email accounts, follow this guide.

With your accounts connected, open the Email Accounts Center and compare health scores across your connected accounts.

If one account dropped, the problem is contained to that sender. 

If every account dropped together, look at the domain they share, or at your copy and targeting.

Tip: Check the Status column, because “Disconnected” and "Permissions Required” both stop an account sending, and you can reconnect in a single click.

Step 5: Rebuild your reputation

Fixing your records can stop the damage, but it doesn’t reverse it. You need to rebuild your reputation.

With Hunter, you can add Inbox Protection to your account. 

This warms the account for you, sending to a private network of real inboxes and generating real replies over 30 days before shifting into ongoing maintenance.

Learn more about Inbox Protection.

To do it free, the Email Warm-Up Calculator builds a day-by-day ramp plan for you based on your provider, current volume, and target. 

Export it to CSV or your calendar and follow the plan by manually sending emails.

Tip: Do not use this email account in your sequence until your ramp-up plan is complete.

Step 6: Follow this pre-send checklist

With all these steps followed, work through this checklist before your next sequence:

• Test: Re-run the Deliverability Checker before a large campaign, then watch for sudden drift in opens, bounces, or replies afterward.
• Ask a question, not for whitelisting: Asking a lead to drag you into their Primary tab rarely works. Ask one specific question instead, because a reply is the strongest positive signal a provider can see.
• Format: Use plain text for your first email in a sequence. HTML emails bounce 652% more, and include your tracking pixel, branded signature, and any link.
• Volume: Keep each account under 50 sends a day. Add more accounts to a sequence rather than raise limits.
• Warm-up: Any account that's new, or unused for 30 days, needs warming before it carries real volume.
• Authentication: Check that your SPF, DKIM, DMARC, and MX all pass the Deliverability Checker.

What this looks like when it's working

Your records pass, your Global Health score climbs as the Deliverability Guard panel empties, and open rates return to their old baseline.

If everything passes and placement still doesn't recover, what's left is your content or your targeting, not your email sending infrastructure.

Put all of this into action today with Hunter's Outreach Planner:

Meet Platt&Co.

Platt & Co. is a consultancy dedicated to helping B2C and B2B pre-seed and Series A venture-backed startups.

Industry

Marketing

Challenge

Generating leads through email

Tools used

Hunter's Domain Search, Email Finder, and Email Verifier

Headquarters

Charlotte, NC, USA

Ran by Juwan Platt, the agency steps in to execute on marketing, sales enablement, design, and product. 

Whether working alongside an existing CMO as an execution arm or directly with a founder, Juwan's goal is to lay the foundation and execute upon their growth strategies.

The challenge

Spotting real business opportunities in the 2020s is tough. 

Juwan noticed that many companies post "ghost postings" or signal to the market that they are growing when they actually are not. 

The rise of AI has only increased the volume of content and information online, making it harder to spot true buying signals. 

Relying on standard job boards like LinkedIn meant wading through hundreds of thousands of posts that weren't always real or serious, creating a severe signal-to-noise ratio problem.

To generate leads, Juwan divided his time among attending events, creating content, and reaching out on LinkedIn.

His email outreach was piecemeal, with him sending as many as 10 emails to get a single response.

The solution

Today, Juwan focuses exclusively on email outreach to generate his leads. His process is simple, meaning that he has an approach that scales while he delivers value to his clients:

1. Authentic hiring platforms: Juwan starts by finding authentic hiring signals on platforms like Wellfound.
2. Spot-check research: He manually reviews every company’s website and leadership team to ensure there’s a need and fit with Platt & Co.’s services.
3. Domain search: With target companies and leads in mind, Juwan uses Hunter’s Domain Search to find CEOs, CROs, and CMOs. 
4. Finding and verifying leads: Whether the lead is found via Domain Search or guessed using an email pattern, Juwan uses Hunter’s Verifier to ensure the email address is correct before sending outreach.
5. Simple outreach emails: Juwan uses HubSpot to send his outreach, sending five to six-sentence emails highlighting his experience and how (& why) he can help
6. No nuisance: If the lead engages with his first email, Juwan sends a concise follow-up to book a meeting; if they don't, he simply moves on.

The results

For Juwan, Hunter isn't just about saving time; it's about pure effectiveness. 

This targeted, Hunter-powered workflow has been the primary engine for his business growth.

In fact, he attributes 90% of his revenue to relationships that began by finding an email through Hunter. 

The remaining 10% comes either from referrals generated by the clients he closed via Hunter. 

His very first major success with the tool years ago even landed Lovesac as a client back when they were just an upstart.

"I honestly don't know a professional life before Hunter."

Juwan Platt, Founder, Platt&Co.

The takeaway

Juwan has used Hunter for nearly his entire entrepreneurial journey, starting around 2016. 

It has entirely shaped his mindset around lead generation, proving that with the right contact information and a concise message, any door can be opened.

As Juwan says himself:

"Every once in a while, you find one of those tools that's just transformational. It's like, oh, well, this just works. That's easy. That’s Hunter." 

Meet Dwellsy

Dwellsy is a platform operating on two main fronts: helping renters find their next rental and landlords get their places rented, and selling comprehensive residential rent data, covering millions of rental units nationwide and providing a first-of-its-kind look at what American renters are actually paying.

Industry

SaaS

Challenge

Finding verified decision maker data

Tools used

Hunter's Domain Search and Email Finder

Headquarters

San Francisco, CA, USA

Their target audience is diverse. 

On one side, they seek property managers to list on their platform. On the other hand, they target data buyers, ranging from financial services and operators to economists and data scientists who need to value real estate or understand the American consumer. 

Because Dwellsy is introducing a new, highly accurate tier of data, its growth relies on finding visionary professionals willing to rebuild their rent models from the ground up. 

Their GTM efforts involve its CEO, Jonas Bordo, and a team including Rae Cunha, Dwellsy’s Marketing Engineer.

The challenge

Identifying the right email addresses for these niche prospects was "really hard". 

The process was defined by constant guesswork, failure, and retrial.

• They frequently used Google to try to find contact information on the open web.
• When that failed, they resorted to guessing email formats, such as assuming a prospect's email was simply "first name dot last name" and testing whether it would bounce.
• They also scoured company websites for contact details, but found this method limited because websites were often not updated as new employees joined.

This manual effort was not just immensely time-consuming; it was actively harming their outreach infrastructure with incorrect information, negatively impacting their email health.

"You know, honestly, it's really hard to find these emails. It was a lot of guesswork and failure and retrial before we found Hunter"

Jonas Bordo, CEO, Dwellsy

Ultimately, this trial-and-error cycle proved unsustainable for a growing sales and marketing engine

The solution

Today, the Dwellsy team uses Hunter dozens of times per day. They approach lead generation through several distinct workflows:

1. Domain search: When the team has a list of target companies, they use Hunter to find the right contacts within those organizations.
2. Verifying data: If they already have a specific prospect's name and domain, they use Hunter's verification tools to ensure accuracy.
3. Organic prospecting: For CEO Jonas Bordo, lead generation is often organic. If he spots an interesting prospect in a news article or through a LinkedIn comment, he immediately uses Hunter to grab their email address and drops it directly into HubSpot to initiate contact.
4. Bulk operations & custom automation: Rae Cunha relies heavily on bulk enrichment to make operations more efficient, batching leads rather than processing them manually. She even built a custom "reverse engineering" automation to infer prospect names based on email confidence levels.
5. CRM sequences: Once verified, the right contacts are added to HubSpot. From there, prospects are dropped into multi-step sequences designed to introduce Dwellsy, offer data samples, or set up conference meetings

The results

The most immediate and quantifiable impact of Hunter for Dwellsy has been the hours they’ve clawed back from manual work.

"I love doing things in a batch, because in bulk it's faster...I am an operations person, and I like to make everything more efficient. So, I use Hunter a lot."

Rae Cunha, Marketing Engineer, Dwellsy

Beyond speed, Hunter delivered a massive upgrade in data quality and transparency, something Dwellsy sees as fundamental across every part of the business.

The team highly values the email address confidence scores inside Hunter, which show exactly how accurate an email is likely to be (e.g., 98% vs. 70%), empowering them to be thoughtful about their sending limits.

Ultimately, knowing that the tool is "just going to work" has enabled Dwellsy to stay in touch with more people, secure more leads, and build a significantly larger set of business opportunities.

The takeaway

Hunter has eliminated the frustration of manual prospecting, replacing it with a reliable, embedded system that drives daily growth. 

It is now considered an essential ingredient in their outreach process.

As Jonas says himself:

"Every once in a while, you find one of those tools that's just transformational. It's like, oh, well, this just works. That's easy. That’s Hunter."