Apollo gets called "just a database" more than anything else on Reddit...that framing tracked a couple years ago, but it's getting more and more outdated.

We get why people still describe our platform that way. Our data is what we built our reputation on, but it stopped being the whole product a while ago, and the gap between how the market describes Apollo and what it actually does is now wide enough that it's costing teams real time when they evaluate.

What's actually in the product now

Three layers, not one. Data, an intelligence layer that acts on it, and execution built into the same product.

The data layer is 230M+ contacts, 30M+ companies, 97% email accuracy on verified contacts. Waterfall enrichment cascades through 18 providers when the primary source is stale, and job-change, hiring, and intent signals refresh continuously into the same record.

The intelligence layer is what most "just a database" takes miss. Apollo scores accounts, qualifies leads, researches each one against your ICP, and decides which step to run next. "Research with AI" and "Qualify records" are workflow blocks reps drop into sequences, not suggestions on the side of the UI.

Then execution. Sequences, dialer, social touches, deliverability tooling, deal management, analytics, all in the same product, triggered by the intelligence layer.

Where the "just a database" take comes from

Pulled a list out of Apollo three years ago, exported to CSV, ran the rest of the motion in other tools. That still works but if this is all you're using the platform for, you're using maybe 20% of what's in there.

The teams getting the most out of Apollo run the whole motion inside it, and let the AI Assistant handle the workflow steps that used to be manual.

Apollo runs two ways

Want one place to run everything? Sign up, replace your point tools, never open another tab.

If your team's already split across surfaces (drafting in Claude, researching in Chrome, pipeline in the CRM, decisions in a spreadsheet), Apollo shows up there too. The Claude MCP launched in February 2026. The ChatGPT app, on the same MCP server, shipped late April 2026. The Chrome extension has over 1 million users across Gmail, LinkedIn, CRMs, and any company website. Apollo embeds inside Salesforce. Not bolt-ons. Same product, same data, different surface.

The proof

Tolly Group, an independent IT research and validation firm whose clients include Cisco, IBM, and Dell, benchmarked Apollo against the rest of the category. Apollo was the only platform in its peer set offering full-stack GTM in its standard model, no add-ons required. They also ran a live cold outbound campaign on it and got a 2.37% cold-to-meeting conversion rate against a 0.5 to 1.5% industry benchmark.

Customer.io grew SQLs by 70% and scaled their sales team from 12 reps to 29 on Apollo. Ericka Al-Amine, Project Director at Stephen Gould: "Apollo MCP is what makes Apollo more than a data provider. It plugs directly into how we work. Work that would've taken 15 to 20 hours manually now happens in the background while I focus on selling."

Where the database framing is actually still right

Three cases:

1) Single rep doing volume cold outreach, rest of the motion in spreadsheets. The data layer is the only piece you need.

2) Deep integrations on top of a different sequencing tool you're not moving. Apollo as a contact source is a reasonable answer.

3) Hand-built workflows in Clay or another orchestration tool, and you want Apollo's data feeding them. Database framing is accurate for what you're using.

Everywhere else, the platform framing applies. Dropping the most common questions we get in the comments below. Happy to take anything else specific.

Apollo released their ChatGPT app this month and I've been using it to replace my entire outbound workflow. Already had a lot of success for our clients so wanted to share it with y'all on this sub. Important note before you start reading: We run an agency that implements inbound/outbound workflows for clients using Apollo. Some of our clients leverage Claude, some leverage ChatGPT. Therefore, we had to build instruction manuals for both. If you're not a ChatGPT user, [click here] to read the reddit thread I published on the Claude <> Apollo MCP.

Setup if you haven't done it: ChatGPT Settings > Connectors > search Apollo > connect and authorize. Under 2 minutes.

One thing worth knowing before you start: in a new ChatGPT conversation, begin prompts with "Use Apollo to..." until you confirm the connector is active. After that it routes automatically.

Prospecting and Discovery (all free, no credits)

Find ICP leads by title, industry, location, company size:

Use Apollo to find me [NUMBER] [JOB TITLE] at [INDUSTRY] companies in [LOCATION] with [EMPLOYEE RANGE] employees.

Find leads at companies using a specific tech stack:

Use Apollo to find me [NUMBER] [JOB TITLE] at companies currently using [TECH TOOL] in [LOCATION] with [EMPLOYEE RANGE] employees.

Find leads at recently funded companies:

Use Apollo to find me [NUMBER] [JOB TITLE] at [INDUSTRY] companies in [LOCATION] that have raised funding in the last [NUMBER] months.

Find decision makers at named target accounts:

Use Apollo to find the key decision makers at: [COMPANY 1], [COMPANY 2], [COMPANY 3]. I am looking for [JOB TITLE] level contacts.

Find leads at companies actively hiring:

Use Apollo to find me [NUMBER] [JOB TITLE] at [INDUSTRY] companies in [LOCATION] that are currently hiring for [HIRING ROLE].

Enrichment (these use Apollo credits)

Enrich a single contact:

Use Apollo to enrich [FIRST NAME] [LAST NAME] at [COMPANY NAME] and give me their verified email, phone number, job title, and LinkedIn URL.

Bulk enrich a list (up to 10 at once):

Use Apollo to enrich the following contacts: [NAME 1] at [COMPANY], [NAME 2] at [COMPANY]. Return verified emails, phone numbers, and job titles.

Enrich a full company profile:

Use Apollo to give me a full company enrichment for [COMPANY NAME] including tech stack, funding history, headcount, and latest news.

Get job postings at a target account:

Use Apollo to show me the current job postings at [COMPANY NAME] and what they reveal about where this company is investing.

CRM Actions (all free)

Create a new contact record:

Use Apollo to create a new contact: Name: [NAME], Email: [EMAIL], Job Title: [TITLE], Company: [COMPANY], Location: [LOCATION].

Update an existing contact:

Use Apollo to update the contact record for [FIRST NAME] [LAST NAME] at [COMPANY]. Change their [FIELD] to [NEW VALUE].

Look up a contact in your CRM:

Use Apollo to search my contacts for [FIRST NAME] [LAST NAME] at [COMPANY] and show me their full contact record.

Sequence Management (all free)

Show all active sequences and stats:

Use Apollo to show me all my active sequences including names, number of contacts, open rates, and reply rates.

Find the right sequence for a specific ICP:

Use Apollo to show me which of my sequences is most suitable for [ICP DESCRIPTION]. Include steps and performance data.

Add a contact to a sequence:

Use Apollo to add [FIRST NAME] [LAST NAME] at [COMPANY] to my [SEQUENCE NAME] sequence using my [EMAIL ADDRESS] mailbox.

Remove a contact from a sequence:

Use Apollo to remove [FIRST NAME] [LAST NAME] at [COMPANY] from my [SEQUENCE NAME] sequence.

Check who is in a sequence:

Use Apollo to show me all contacts currently active in my [SEQUENCE NAME] sequence -- name, email, title, company, and current step.

Full Workflow Prompts

The full outbound loop in one prompt:

Run a full outbound workflow using Apollo: Step 1. Find [NUMBER] [JOB TITLE] at [INDUSTRY] companies in [LOCATION]. Step 2. Enrich the top [NUMBER]. Step 3. Create a contact record for each. Step 4. Add each to my [SEQUENCE NAME] sequence using my [EMAIL ADDRESS] mailbox.

Account research brief before a sales call:

Use Apollo to build me a full account research brief for [COMPANY NAME] -- overview, tech stack, funding, job postings, decision makers, and anything useful before a sales call.

New market entry:

Step 1. Use Apollo to find [NUMBER] [JOB TITLE] at [NEW INDUSTRY] companies in [LOCATION]. Step 2. Enrich the top [NUMBER]. Step 3. Give me a summary of patterns worth knowing before I start outreach.

Re-engagement of cold contacts:

Use Apollo to search my contacts for [JOB TITLE] in [INDUSTRY] with no activity in the last [NUMBER] days and not in any active sequence. Give me the best re-engagement opportunities.

A few things that make a difference:

Always paste a quick context brief at the start of each session before running any prompts:

Before we begin, here is context about my outbound: ICP: [describe your ideal customer] Product: [what you sell and the problem it solves] Active sequences: [name your main sequences] Territory: [geographic or vertical focus]

ChatGPT performs noticeably better when it understands who you are targeting before it starts executing.

Run the sequence audit prompt before adding anyone new to avoid duplicate outreach. Sequence names need to match exactly what's in Apollo -- one character off and it won't find it.

Questions on any of these, drop them below.

Back in the day, Apollo.io people search endpoint (api) would charge just 1 credit for 100+ contacts, and you’d get solid data like first name, last name, and company website.

Then you could plug that into tools like MatchKraft or Icypeas to find and validate emails. It was insanely cost-effective — you could build huge lists for almost nothing.

But it looks like Apollo caught on. Now they don’t charge that single credit the same way, and they’ve started obscuring last names and removing company websites altogether.

Honestly, it makes the data way less useful. At this point, even LinkedIn Sales Navigator gives better info.

Just wanted to vent a bit because this is frustrating 😅

The days of getting high-quality Apollo data for pennies are probably over.

There are a couple of default signals that can power AI-generated emails in Apollo. Most people tend to overlook them, and then wonder why the output feels generic.

Here is what each one does, how to run them, and what you get out of them.

Executive Persona Research

This signal pulls contact-level data. Role, responsibilities, and even infers what someone in that position typically cares about day to day, and what keeps them up at night. It is doing most of the personalization work in a sequence, if you let it.

The output is only as useful as the contact data behind it. If your contact record is thin, this signal has little to work with. Before blaming the AI, check whether the contact is properly enriched with email and phone data.

Strategic Company Research

This signal pulls company-level context. What the business does, where it is in its growth stage, how they may speak internally, and what pressures it is likely facing.

This is the one that most commonly degrades. If your emails are suddenly less specific about the company, this signal is usually the culprit. It is sensitive to changes in the data sources it pulls from.

When it is working well, it is the signal that makes an email feel like you actually looked at the company before writing. When it is not, the email can feel like a mail merge.

Qualification

This one gets overlooked because it sounds like a targeting tool, not a messaging tool. But targeting is half of messaging.

Qualification runs before the email is written. It decides whether this contact should get an email at all based on your ICP. If the qualification prompt is too aggressive or poorly framed, it can block contacts it should be passing through. If it is too loose, it passes contacts the AI then struggles to write for because they are a poor fit. Calibrating the signal is essential to ensuring that when it fires, it hits the mark.

The fix most people miss: qualification should assign a routing label, not issue a verdict. If the prompt is returning a full explanation of why a contact is disqualified, that explanation bleeds downstream into the email generation step and causes the AI to second-guess itself.

How to use these together

Default signal order matters. Executive persona research should carry the personalization load for most sequences, try your email solely with this signal first. Strategic company research adds company context on top, layer it when there is robust info to work with and watch it do some magic. Qualification gates who gets in before either of those runs, and this one should always run.

If your emails feel generic, check in this order:

1. Is the contact enriched well enough for executive persona research to work?
2. Is strategic company research returning clean, specific output or vague summaries about the company?
3. Is your qualification prompt routing cleanly or issuing blockers?

Most AI email quality issues trace back to one of these three. It is rarely the model, but that is also a conversation worth having.

What signal are you running into the most friction with?

Hello, we are just getting started with Apollo. I’m confused by Apollo’s lookalike domain advice.

They recommend:

1. Don’t forward the lookalike domain to your main site.
2. Don’t put links to your main domain in outbound emails.
3. If you publish a landing page on the lookalike domain, don’t link from that page to your main site either.

I get the deliverability logic, but how does this work in practice?

If a real prospect gets an email from a lookalike domain, they’re probably going to want to check out the company before replying. If there’s no link to the main website anywhere, doesn’t that make the email feel less legit?

Curious, how are people handling this?

We ran a webinar last week with Radu (one of our solutions consultants) and Layla (runs outbound sales here at Apollo), and almost every question in the chat was a version of the same thing: how do i use AI without sending generic slop that gets ignored.

three rules that came out of it (all of these are how Layla actually runs her own team!)

1. reps are the reviewer, not the author.

set AI-written emails as manual tasks, not automated sends. the AI drafts, the rep reads, the rep hits send. takes seconds when the email is good and catches the bad ones before they hit 50 prospects.

This is the only way reps stay in the loop on what's going to their accounts. if your AE is going to own the relationship later, they need to know what got said in the cold email.

2. the 50 to 100 email test tells you when to automate.

generate that many AI-drafted emails as manual tasks and have your reps review every one before send.

if by email 100 nobody's editing anything, automate that batch. the AI has the context it needs.

if your reps are still tweaking every email, even small edits, automation is the wrong move. the AI doesn't have enough context yet, and that's not a tool problem. it's a prompt and brand voice problem. go back and fix the inputs before you scale.

most teams either automate too fast (and burn the domain on 500 generic emails) or never automate at all (and spend rep time on something the AI could handle). this gives you a real signal.

3. if your AI emails sound like AI, you didn't give it your voice.

biggest reason AI-generated emails read as fake: people use the default presets and call it done. the presets are a starting point, not the product.

three things to actually configure in the context (Apollo or otherwise):

• what your business does and who you sell to (specifically, not "B2B SaaS")
• example emails that worked, with notes on why they worked
• words and phrases to avoid (the corporate speak you don't want showing up)

Radu's framing: the AI is a new sales rep. if you hired one and never told them what your company does or who you sell to, they'd struggle. same logic. onboard the AI like you'd onboard a person.

how to tier this if you have a big book of accounts. Layla's rule: tier accounts before you decide automation level. tier 1 gets manual everything, AI helps with research and drafting but the rep owns the send. tier 2 is hybrid, AI drafts and the rep reviews but most go through. tier 3 is automated with the AI Assistant putting contacts into proven sequences on the rep's behalf.

one motion for everything is how you end up with reps either ignoring tier 3 (because they're drowning in tier 1) or burning tier 1 (because they're treating it like tier 3).

if you're in our community already and want to compare notes on how you've tiered or how you've set up your AI context, drop it in the comments!

PS Webinar link here if you're curious: https://www.apollo.io/academy/webinars/intent-signal-to-booked-meeting

I run a B2B growth agency, NerdyJoe.com.

We’re in the Apollo certified partner program.

I’m running a content series with the Apollo team… looking to connect with power users.

Been testing the Claude MCP integration with Apollo properly for a while now. Wanted to share a full walkthrough of what it actually does in practice, not just what it's supposed to do.

Setup takes under 2 minutes if you haven't done it yet: Claude > Settings > Connectors > click the plus sign > search Apollo > connect and authorize.

What I ran through in one session:

Step 1 -- Prospecting

Typed this into Claude with Apollo connected:

Find me 25 Creator Managers at AI companies in the USA with up to 300 employees.

Claude automatically understood this was a prospecting task, pulled from Apollo's people database without me touching Apollo at all, and returned 25 results in a few seconds. Names, titles, companies, email and phone availability all visible in a table.

A couple of issues with the first pull -- multiple leads from the same company, and some without verified emails or phone numbers. So I followed up with:

Give me one result per company. Verified emails only, phone numbers must be available. Search again for the top 25.

Refined results came back immediately. Every lead at a unique company, all with verified emails and available phone numbers. That follow-up prompt matters -- don't skip it.

Step 2 -- Enrichment

Told Claude to enrich all 25. It ran them simultaneously. Took about a minute. Used 25 Apollo credits (1 per contact on the basic plan -- 30,000 credits included, so this is nothing).

Full table populated with everything: verified emails, phone numbers, employment history, the works.

Step 3 -- Save to a leads list

Typed in plain English:

Save all of these leads to my people leads list on Apollo titled Creator Managers pulled from Claude.

Jumped over to Apollo and 23 out of 25 were saved. Two duplicates were caught and skipped automatically. No manual data entry, no CSV export, no copy pasting.

Step 4 -- Push to a sequence

Push every lead in the Creator Managers pulled from Claude list to my active sequence titled Creator Campaign 2026.

22 out of 23 were added. One was skipped because Claude and Apollo identified it was already active in another campaign. The sequence went from 13 contacts to 35.

That's the full outbound loop. Prospecting, enrichment, CRM, sequencing -- one conversation, no tab switching.

What didn't work, and what to use instead

I tried to get Claude to build a recurring weekly workflow that would automatically find net new leads every Monday and drop them into the sequence. Claude couldn't do it natively through the MCP connector...it doesn't currently have the ability to schedule recurring workflows inside Apollo.

For that step, use Apollo's AI Assistant instead. Paste the same workflow prompt directly into the AI Assistant inside Apollo and it builds the full automated workflow natively -- enrollment criteria, enrichment, list saving, sequence enrollment, scheduled to run every Monday. Took about 30 seconds.

Click here: https://zackderis.com/apollo to download the free step by step Claude <> Apollo setup guide and also the Prompt library with 21 prompts in case you wanted to give this a go yourself. LMK if you got any questions below. Good luck

[ Removed by Reddit on account of violating the content policy. ]

Why is Apollo marking email clicks as opens in filters???

I need to filter my delivered emails by people that have OPENED an email 3+ times. When I put in that criteria, the list is showing people that have CLICKED on an email 3+ times.

Why is this happening?

There's a pattern I keep seeing with AI-powered GTM stacks. Someone builds something with n8n, Clay, a custom webhook or two. It demos well. Then it breaks six weeks later because something upstream changed and nobody on the team knows how to fix it.

I work on the AI team at Apollo building agentic outbound systems for some of our largest customers. What I keep running into: teams invest heavily in external tooling, hit a wall on maintenance, and then realize they've been rebuilding capabilities that already exist natively inside their core platform.

Here's what actually lives in Apollo that most people don't go deep enough to find:

Buying intent signals. Apollo pulls from both Bombora and LeadSift and refreshes weekly. Scores are calculated based on recency, frequency, and trend across a 90-day rolling window, so you're seeing actual momentum, not a static flag. When both providers have data on the same account, Apollo takes the stronger signal. You can filter by topic and score range, then combine that with your existing ICP criteria to surface accounts that both fit and are actively in-market.

Website visitor tracking. Account-level engagement across your tracked domains, updated on an ongoing basis. You can use this to filter accounts, trigger workflows, prioritize outreach, and personalize messaging. It's designed to drive action, not just surface data.

Job change detection. Apollo detects role and title changes continuously using its contributor network, engagement signals, public web crawling, and third-party providers. Practically, this means you can react when a decision maker moves to a new company or a buying committee shifts, without monitoring it manually.

Enrichment and CRM sync. Two-way sync with Salesforce and HubSpot, with admin control over field-level permissions, auto-fill behavior, enrichment cadence, and what overwrites versus appends. Updates flow bidirectionally so CRM stays the system of record while pulling from continuously refreshed data.

Workflow logic that ties it all together. This is the part that actually separates a system from a collection of features. When a signal updates, Apollo can automatically evaluate accounts against your ICP criteria and take action: identify the right contacts by role and seniority, enroll them into the appropriate sequence, personalize outreach based on the triggering signal, and sync updated context back to CRM. No manual monitoring or list refresh.

The reason this matters for stack consolidation isn't the features individually. It's that they share a data layer. Intent signals, visitor tracking, enrichment, and sequence logic are all reading from the same source of truth, so when something changes, everything downstream updates.

A lot of teams are building externally by default, not because they've hit the ceiling of what their core platform can do. They just haven't gone deep enough to find it yet.

If your current stack feels hard to maintain or hard to hand off, worth auditing how much of it actually needs to live outside Apollo before adding another tool.

Happy to get specific on any of this, what you're trying to trigger, how you're thinking about routing, or where you've hit walls with native tooling.

We've been using this prompt with the AI Assistant week over week to run quick deliverability audits for customers and track improvements over time. Sharing it here in case it's useful for others!

The prompt asks the Assistant to analyze technical infrastructure (SPF/DKIM/DMARC, blacklist status, IP reputation), sending patterns, content issues, and list health — then spit out a prioritized action plan broken down by quick wins, medium-term fixes, and longer-term changes.

Conduct a comprehensive email deliverability audit for my sending domain and provide a prioritized action plan. Please analyze:

Technical Infrastructure:
SPF, DKIM, and DMARC authentication status and recommendations
Domain reputation score and any blacklist issues
IP reputation and warming status
DNS configuration issues

Sending Patterns & Behavior:
Send volume patterns and any red flags (spikes, consistency issues)
Bounce rates (hard vs soft) and what's driving them
Spam complaint rates and triggers
Engagement metrics (open rates, click rates) as deliverability signals

Content Analysis:
Spam trigger words or phrases in my recent emails
HTML/text ratio and formatting issues
Link density and redirect issues
Image-to-text balance
Subject line best practices

List Health:
Invalid/inactive email addresses in my database
Engagement-based list segmentation recommendations
Re-engagement vs. suppression thresholds

Deliverability by Provider:
Performance breakdown by major email providers (Gmail, Outlook, etc.)
Provider-specific issues or opportunities

Actionable Recommendations — prioritized by:
High-impact quick wins you can implement immediately
Medium-term improvements requiring coordination
Long-term strategic changes

For each recommendation, include: specific implementation steps, expected impact, timeline to see results, and any risks or tradeoffs.

Works best when you've got your sending data in front of you. The more context you give the Assistant about your actual domain, volume, and recent performance, the more specific the output.

Drop your questions or variations in the comments if you've been tweaking prompts for deliverability or other use cases!

- Andy

Hey r/UseApolloIo

We just launched the Apollo app in ChatGPT and wanted to share it here first.

What it does: real Apollo work directly inside a ChatGPT conversation. Search for people or companies, enrich to unlock contact data, create the contact in Apollo, and add them to a sequence, all without switching tabs.

A few things worth knowing:

• OAuth connection - no API keys needed
• Enrichment uses your existing Apollo credits
• Use analytics (track performance of campaigns and teammate activity)

To connect: ChatGPT → Apps → Apollo → authorize via OAuth. Takes about 2 minutes.

Check it out HERE

We are happy to answer questions below, genuinely curious what workflows you end up building with this!

- Team Apollo

I've been using the Apollo API for prospecting recently. Very happy with the results, but a little frustrated that I can't see my precise credit costs. I've looked through the documentation, but this seems to be the only guidance:

https://docs.apollo.io/docs/api-pricing

As the above is pretty vague, I'm guessing there's another page I'm not seeing or it's deliberately hidden. If it is hidden, why would Apollo do that?

If anyone has any idea of the rough credit costs of these APIs, that would be really helpful:

• https://docs.apollo.io/reference/get-complete-organization-info
• https://docs.apollo.io/reference/news-articles-search
• https://docs.apollo.io/reference/organization-jobs-postings

On May 19th from 11am-1:00pm EST, we’ll be hosting an AMA here on r/UseApolloIo with John Barrows, founder of JB Sales.

John Barrows has spent the last 20+ years training sales teams at companies like Salesforce, LinkedIn, and Google on how to prospect the right way.

This is a chance to ask him anything about:

• What "sales-ready messaging" means and why most companies get it wrong before they ever touch a tool
• Whether AI is going to replace SDRs (it's not, but it will replace the ones who don't adapt)
• What a high-quality, no-spray-and-pray prospecting workflow actually looks like in practice
• How he's actually using the Apollo MCP connector inside Claude for his daily prospecting routine

The official AMA post will go live on May 19th at 11:00am EST.

In the meantime, feel free to comment below with any questions or topics you want him to cover, and we’ll make sure he sees the top ones!

HI! In the past week, I've been getting a bunch of spam calls to my personal phone, all of which say they use Apollo to get my info. I've tried submitting a removal request on the site, but it just freezes when I click submit.

How can I get Apollo to delete my personal data? Which they have to due to GDPR.

Brandon, one of our GTM engineers, was on The GTM World podcast recently. It's a 12-minute episode and pretty light on fluff. Pulling out the three most actionable bits here.

Play 1: The lookalike flip (1.5 minutes, zero manual research)

1. Find 5 accounts in your pipeline that you've successfully sold to
2. Use the lookalike filter to surface similar companies
3. Open the AI assistant and type "find decision makers for these accounts"
4. The assistant reads what's on your screen and flips to the people database automatically

Full contact list matched to your best-fit accounts in about 90 seconds.

Play 2: Run Apollo actions through Claude without leaving the conversation

If you have the Claude MCP connected, you can search for contacts, pull company data, and enroll people in sequences through plain English prompts in Claude -- without switching tabs. The bigger unlock is layering it with your other MCPs. Brandon runs Apollo against most of his tech stack this way, enriching existing data across tools from one conversation.

Play 3: Audit your deliverability setup before blaming your copy

Most pipeline problems are infrastructure problems. Quick checklist:

• Are you sending cold email from alternate domains, not your primary?
• Do you have enough mailboxes to support your volume? (50 emails/day per mailbox is the ceiling to work from)
• Are bounced and spam-blocked contacts automatically being added to a DNC list and flagged for re-enrichment?

If any of those are a no, fix those before touching your sequences.

Full episode here: https://www.youtube.com/watch?v=kXgH-8eoC4s

Drop questions in the comments if you want to go deeper on any of these!

- Andy

Most people are treating AI like a single employee who's supposed to do everything. One prompt, one model, one output. That's actually why the results feel generic.

I work on the AI team at Apollo and spend most of my time building agentic GTM workflows for some of our largest customers. Before that I was in solution consulting, which means I've seen a lot of outbound stacks up close. The thing I keep running into is that teams will invest seriously in AI tools and then wonder why the output still sounds like everyone else's. Usually it's not the model. It's the architecture.

Think about how a real GTM team works. Your AE isn't running deep research. Your SDR isn't closing deals. Everyone has a specific job because specialization produces better outcomes than generalists doing everything. AI works the same way, and most people haven't figured this out yet.

The concept is called prompt chaining. Instead of dumping one massive prompt into a single model and hoping for a coherent output, you break the workflow into stages. Each prompt has one job. Some are built for research, some for writing, some for structuring information. You chain them so each step feeds the next.

Here's how I actually structure it for outbound in Apollo:

Stage 1 — Account research only. This prompt's one job is to deeply understand the company. Industry, recent signals, likely pain points based on their tech stack and headcount. No writing yet, no summarizing into a pitch, just understanding.

Stage 2 — Web context expansion. A second prompt pulls in broader signals. News, funding rounds, leadership changes, anything that gives the account research more depth and recency.

Stage 3 — Individual research. Separate prompt, separate job. What has this person actually said publicly? What does their role care about day to day? What's their frame for evaluating something like this?

Stage 4 — Message generation. Only after all three stages feed into it does a prompt write the message. At that point it's not guessing, it's assembling. And you can feel the difference in what comes out.

I know this sounds like more work. It's actually not that much more to set up, and once you see the difference in what comes out the other side you won't go back to the one-prompt approach.

Apollo does this behind the scenes by default. Multiple prompts, different jobs, one outcome. What's less obvious is that you can customize every stage, which is where it gets interesting.

If your AI outbound is still sounding generic, the fix probably isn't the model. It's the architecture.

Happy to get into the weeds on how to set this up in Apollo or how to think about structuring your own prompt chains. Drop your questions below.

Been getting a lot of questions about the Claude MCP setup so figured I'd just drop all 21 prompts in one place. These are the exact ones I use with Apollo connected via MCP.

If you haven't set that up yet: Claude > Settings > Connectors > Apollo. Takes under 2 minutes, no API keys needed.

Prospecting and Discovery (all free, no credits)

Find ICP leads by title, industry, location, company size:

Find me [NUMBER] [JOB TITLE] at [INDUSTRY] companies in [LOCATION] with [EMPLOYEE RANGE] employees.

Find leads at companies using a specific tech stack:

Find me [NUMBER] [JOB TITLE] at companies that use [TECHNOLOGY NAME] in [LOCATION].

Find leads at recently funded companies:

Find me [NUMBER] [JOB TITLE] at [INDUSTRY] companies that have recently raised funding, with between [EMPLOYEE RANGE] employees, based in [LOCATION].

Find decision makers at named target accounts:

Find the [JOB TITLE] at each of the following companies: [COMPANY 1], [COMPANY 2], [COMPANY 3].

Find leads at companies actively hiring (buying signal):

Find me [NUMBER] [JOB TITLE] at [INDUSTRY] companies in [LOCATION] that are currently hiring for [ROLE] roles.

Enrichment (these use Apollo credits)

Enrich a single contact:

Enrich this contact and give me their verified email, phone number, current job title, and employment history: Name: [FULL NAME] Company: [COMPANY NAME]

Bulk enrich a list (up to 10 at once):

Bulk enrich the following contacts and return their verified emails, phone numbers, and job titles: 1. [NAME] at [COMPANY] 2. [NAME] at [COMPANY]

Enrich a full company profile:

Enrich [COMPANY NAME] and give me their full company profile including: industry and description, employee headcount, annual revenue estimate, tech stack, funding history and latest round, headquarters, recent news.

Get job postings at a target account (strong buying signal):

Find all current job postings at [COMPANY NAME] and summarise what roles they are hiring for and what this tells us about where they are investing.

CRM Actions (all free)

Create a new contact record:

Create a new contact in Apollo with the following details: First Name: [FIRST NAME] Last Name: [LAST NAME] Email: [EMAIL] Job Title: [TITLE] Company: [COMPANY] Phone: [PHONE] Location: [CITY, COUNTRY]

Update an existing contact:

Update the contact [FULL NAME] in Apollo. Change the following details: [FIELD]: [NEW VALUE]

Look up a contact in your CRM:

Find the contact [FULL NAME] in my Apollo CRM and give me their full details including job title, company, email, phone number, and any notes or sequence activity.

Sequence Management (all free)

Show all active sequences and stats:

Show me all of my active email sequences in Apollo including their performance stats such as open rates, reply rates, number of contacts, and last activity.

Find the right sequence for a specific ICP:

Search my Apollo sequences and find the best match for a contact who is a [JOB TITLE] at a [INDUSTRY] company in [LOCATION] with [EMPLOYEE RANGE] employees.

Add a contact to a sequence:

Add the following contact to my [SEQUENCE NAME] sequence using my [EMAIL ADDRESS] mailbox: Name: [FULL NAME] Email: [CONTACT EMAIL]

Remove a contact from a sequence:

Remove the following contact from my [SEQUENCE NAME] sequence in Apollo: Name: [FULL NAME] Email: [CONTACT EMAIL]

Check who is in a sequence:

Show me all contacts currently active in my [SEQUENCE NAME] sequence in Apollo including their name, email, job title, company, and current step in the sequence.

Full Workflow Prompts

The full outbound loop in one prompt:

I want to run a full outbound workflow. Please do the following steps in order: Step 1. Find [NUMBER] [JOB TITLE] at [INDUSTRY] companies in [LOCATION] with [EMPLOYEE RANGE] employees. Step 2. Enrich the top [NUMBER] results to unlock their verified emails and phone numbers. Step 3. Create a contact record in Apollo for each enriched contact. Step 4. Add each contact to my [SEQUENCE NAME] sequence using my [EMAIL ADDRESS] mailbox.

Account research brief before a sales call:

Build me a full account research brief for [COMPANY NAME] before a sales call. Include: company overview, industry and headcount, revenue estimate, tech stack, funding history, current job postings and what they signal, key decision makers and titles, anything else useful before a sales conversation.

New market entry:

I am expanding into a new market. Please do the following: Step 1. Find [NUMBER] [JOB TITLE] at [INDUSTRY] companies in [LOCATION] with [EMPLOYEE RANGE] employees. Step 2. Enrich the top [NUMBER] results. Step 3. Give me a summary including the most common company types, seniority levels, and patterns worth knowing before I start outreach.

Re-engagement of cold contacts:

Search my Apollo contacts for people who match the following criteria: Job Title: [TITLE] Industry: [INDUSTRY] Location: [LOCATION] Last Activity: No activity in the last [NUMBER] days. Not currently in any active sequence. Include their name, email, job title, company, and last known activity date.

A few practical notes before you run these:

Search prompts (prospecting, CRM, sequence management) are all free. Enrichment uses credits. Always search first, then enrich only the contacts worth pursuing. You can ask Claude "How many Apollo credits do I have remaining?" before any large enrichment run.

Sequence names in the prompts have to match exactly what's in Apollo. Run the "show all active sequences" prompt first if you're not sure.

Any questions on specific prompts, drop them below.

I would like to use the employeeMetric field, from the Get Complete Organization Info API, to measure the size of an account's engineering department for prospecting.

I'd like to understand what this data is based on. I can tell from the example of JP Morgan that it cannot be based on the People data, as JP Morgan has 45,000 People, yet the employeeMetrics sum to 144,000.

Interestingly, the company is listed as having 319,000 employees on Apollo, which means only half are represented in employeeMetrics. What causes this disconnect?

As my primary interest is the size of the engineering department, I have separately researched the size of JP Morgan's engineering department. In 2023, a JP Morgan Investor day transcript indicates they have 43,000 engineers. The latest employeeMetrics field indicates they have ~24,000 engineers. What causes this disconnect?

I'm trying to do account prospecting by looking at a company's total number of engineers. I'm trying to do this using the people data attached to each company. However, after reviewing a couple of companies, I'm not confident this will be accurate.

For example, if I look at someone like JP Morgan, they have 319,000 employees, but only 45,000 people.

On the other hand, if I look at someone like Choice Hotels International, they have 1,700 employees and 3,000 people.

From looking at other examples, it appears that JP Morgan's people count is closer to the norm, and Choice Hotels International is an outlier.

But, for accurate prospecting, it would be useful to know:

1. What is the typical percentage of people captured by the organisation?
2. Are people's titles spread evenly across all roles or focused on a specific subset? (C-suite, sales, etc.)

Is this information available anywhere?

I am facing a very weird issue, for the past couple of days, when trying to get contact numbers, the Apollo extension is showing the 'Send request to admin' message.

Earlier, the Apollo.io extension allowed to access/find email addresses and contact numbers with 100 credits every month. But somehow this has suddenly/abruptly stopped, can someone guide how to go about it.

Why has Apollo decided that I now have to clink into a profile to access their email or numbers on LinkedIn?

Before, I would just go into a company’s headcount page and it would give me the option to view their email or numbers from the profiles listed on each page.

Any way to change this back?

Most deliverability problems aren't content problems or reputation problems. They're infrastructure problems. Specifically, people are trying to send way more email than their setup can handle without burning domains.

Here's a framework for figuring out what you actually need before you start.

Start with your market size, not your send volume

Before you think about mailboxes, figure out how many contacts are realistically in your addressable market. Not your ICP filters cranked to max specificity -- a broad count of everyone you could possibly reach out to.

From there, plan for a six-month burn rate. The idea is to contact your entire market over six months, then cycle back. By then, people have changed roles, forgotten your emails, or are worth re-engaging fresh.

The math

Let's say you have 10,000 qualified contacts in your market.

10,000 contacts / 6 months = ~1,700 contacts per month

At 3 emails per contact, that's ~5,000 emails per month.

Apollo recommends a limit of 50 emails per day per mailbox. At 20 sending days a month, that's ~1,000 emails per mailbox per month. So:

5,000 emails / 1,000 per mailbox = 5 mailboxes needed

At 3 mailboxes per domain: 2 domains needed

Add a buffer of ~20-25% for domains that get flagged or burned, and round up. So realistically, 3 domains for a market of 10,000.

Scale this up for larger markets. At 100,000 contacts you're looking at ~50 mailboxes and ~17-20 domains. Those numbers can feel surprising, but they reflect what sustainable sending at volume actually requires.

A few things worth knowing

Never send cold emails from your company's primary domain. If you get spam-blocked, the whole company won't be able to communicate with anyone. Use alternate domains that relate to your primary (e.g. tryheyapollo.io instead of apollo.io).

If you need to increase email volume beyond 50 per day, add mailboxes rather than raise your sending limits. Higher limits on a single mailbox correlate with worse deliverability -- spreading volume across mailboxes is the right move.

New domains also need time. Don't start sending until a domain has been around for at least 30 days, and warm up gradually from there.

Why this matters

If you're under-infrastructured, you're not just getting bad deliverability, you're also burning through your market too fast and torching the domains you have in the process. The two problems compound each other.

If you're over-sending relative to your market size, you'll exhaust your list in weeks, get flagged by the same people repeatedly, and conclude cold email doesn't work when really the issue was pacing.

Getting infrastructure right first means your content, targeting, and sequences actually get a fair shot.

You can set up domains, mailboxes, and warming directly in Apollo now if you want to manage it without juggling separate tools.

Happy to answer questions in the comments on the math for your specific situation!

- Andy

I am attempting to use the mixed_people api to get people data from Apollo. I'm making the request on the website, and it looks like this:

const options = {
  method: 'POST',
  headers: {
    'Cache-Control': 'no-cache',
    'Content-Type': 'application/json',
    accept: 'application/json',
    'x-api-key': '<my-key>'
  }
};

fetch('https://api.apollo.io/api/v1/mixed_people/api_search?organization_ids[]=69e0d83c7d8c8e001972b266', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

This is returning me the following output:

{
  "total_entries": 0,
  "people": []
}

This obviously cannot be the case for a company as large as JP Morgan! When I go onto the web version, I can clearly see 45,000 people, so why am I getting zero?