u/NoahFromApollo

Most people think of Apollo as a data platform. You go there, pull contacts, get account info, move on...but we built something a little different.
I work on the AI team at Apollo and I just finished putting together a journey-based renewal series for our account managers that uses Apollo as the actual orchestrator of the whole workflow and not just the data source.

Here's how it works:

The anchor: Salesforce

We pull contract end dates from Salesforce and use those as the anchor for everything downstream. From there, Apollo kicks off a series of timed flows that notify the AM at the right moments leading up to the renewal.

At each stage, four things happen in sequence:

1. Apollo runs AI research on the account and surfaces strategic guidance: who to reach out to, how to position the conversation, what the renewal narrative should be.
2. The right contact (the admin at the account) gets enrolled into a sequence automatically.
3. A Slack message goes out to a shared channel where the AM and their manager can both see it.
4. That Slack message includes a link to the account, what the rep needs to do, the AI guidance, and a direct link to the sequence task.

The interesting part: the Claude project

The Slack message also links to a Claude project that is connected to Snowflake data and Apollo's MCP. The rep opens it, pastes in a prompt, and Claude generates the actual artifact (executive brief, 6-month EBR, or end-of-year EBR) and handles creating the Google Slide deck.

Here is what the actual Slack notification looks like:

u/Account Manager. Contract ends on [Date]. Time to send the Executive Brief
to [Account Name].

AI strategic guidance on who to reach out to and how to position the brief:

Strategic angle: With [X] seat upside identified and a team actively ramping
new reps, this check-in is best positioned around ensuring the team has the
foundation to hit pipeline targets as the renewal approaches. Demonstrating
measurable outbound progress from the ramp investment strengthens the
renewal narrative.

Who to reach out to: Reach out to [Contact Name]. They are directly
overseeing end-user ramp sessions and have visibility into team performance
and capacity gaps that justify expanding the seat count.

Next steps:
  1. Review the contact added to the sequence via the link below
  2. Replace [email] with the correct recipient
  3. Prompt Claude with: "Executive summary for [Account ID] to [email]"

Sequence task: [link]
Claude Project: [link]

Prerequisites: Snowflake access approved, Snowflake MCP connected,
Apollo MCP connected.

The whole thing is measurable! Contacts are in sequences, tasks are logged, reps have a clear action every time they get pinged.

Happy to share the Claude project file if anyone wants to set up something similar. Drop a comment.

Noah

Here's a closed-won lookalike outbound play I built recently that I want to share because it worked and more teams should be running something like this.

The concept came from a colleague. The idea: use your closed-won deal data to identify patterns in the companies and personas you've actually helped, then use those patterns to find the next hundred accounts that look exactly like them.

Here's how I built it.

Step 1: Pull your closed-won data from Snowflake
I ran SQL queries against our Snowflake instance to pull closed-won deals, the company attributes that made them a fit, and the personas involved in the sale. Downloaded the CSVs and uploaded them to Claude Code.

Step 2: Build lookalike audiences in Apollo
Apollo has a lookalike company field in Company Search and a lookalike People Search. I used the closed-won company data to seed these searches and find accounts with similar firmographic profiles, industry mix, tech stack, and growth signals to our best customers.

Company looksalikes is based on deals that have closed > a specific threshold with context associated to the deal such as identified pain and key metric such as jobs to be done.

People lookalikes is based on the identified champions on the deal.

Step 3: Route contacts to the right rep
Once the lookalike contacts were identified, I routed them to reps based on account ownership in Apollo. No manual reassignment or spreadsheet.

Step 4: Add closed-won context as a custom field
This is the part that makes the outreach actually land. I pulled the relevant closed-won context from the deal data and placed it into a custom field on the contact record. Which customer segment they match, what problems that segment typically has, what outcomes we drove there.

Step 5: Generate personalized outreach using that context
With the custom field populated, Claude generated a personalized message for each contact grounded in the specific closed-won context that matched their profile. Not "we've helped companies like yours." Actual specifics about the use case, the outcome, and why this prospect fits the same pattern.

One hard rule: customer names never appear in the outreach. The context informs the message but the message stays compliant.

The whole thing was built through Claude Code with no engineering involvement, no custom data pipeline, no manual list building. Still early and a lot to tighten up, but the infrastructure is there.

The piece I'm genuinely excited about is we're working with our Product team to make signal-based plays like this available natively in Apollo, so anyone can launch them without touching SQL, APIs, or raw data!

Happy to get into specifics on the Snowflake query structure, the lookalike field logic, or how to set up the custom field routing.

Noah

TLDR: Use Claude Projects to automatically interact with your book of business in a predefined way that actually works.

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I work on the AI side at Apollo building agentic GTM workflows. A few weeks ago I built a Claude project that automatically scores accounts every Monday using live buying signals pulled directly from Apollo. Setup takes about 15 minutes. Here's the full breakdown of how it works and how to build it yourself.

This workflow is especially useful for reps or account teams that already operate from a predefined book of business. Instead of prospecting random net-new accounts or accidentally stepping on someone else's territory, the workflow stays scoped to accounts assigned to a specific rep or owner inside Apollo. The goal is to help prioritize where to spend time each week, identify the best opportunities inside owned accounts, and uncover untouched contacts that are actually worth engaging.

What it does once it's running

Open the project at the start of your week and Claude immediately starts working with no prompt needed. It pulls owned accounts from Apollo, enriches the top 30 with buying signals, scores each account 0–100, and surfaces the top 10–20 with verified contacts not already in a sequence.

The scoring model weighs four signal categories:

Buying intent signals (30 pts)

Is the account actively researching topics related to what you sell? Spike in website visitors in the last 30 days?

Hiring and growth signals (25 pts)

Are they hiring for personas that match your buyers? Did they raise funding in the last 6 months?

New hire signals (20 pts)

Did a new decision-maker join in the last 90 days? This one is weighted heavily because new execs often mean fresh budgets and open vendor decisions.

Persona availability + firmographic fit (25 pts)

Are there verified contacts matching your buyer profile who are not already in a sequence? Does the company size match your ICP?

After ranking the accounts, Claude surfaces up to 3 real, verified contacts per account. People with confirmed emails who have not been touched yet. It then prompts you to take action: add them to a sequence, do a full account deep dive, draft a personalized email, or re-run with different filters.

Step 1: Connect Apollo MCP to Claude

Go to claude.ai → Settings → Integrations. Search for Apollo.io and authorize with your Apollo credentials. Confirm it shows as Active before moving on.

Important: You need API access enabled on your Apollo plan. If you hit an authorization error, your Apollo admin needs to enable MCP access for your user specifically. It is not on by default for all plans.

Step 2: Find your Apollo User ID

Log into Apollo, click your avatar in the bottom-left corner, go to Settings → My Profile. Look at the URL in your browser:

https://app.apollo.io/#/users/[YOUR_USER_ID]

Copy everything after the last slash. You'll need this so Claude knows whose account ownership rules to pull against.

Step 3: Create a new Claude project

Go to claude.ai → Projects → New Project. Name it "Weekly Prioritization".

In project settings, enable Apollo MCP. Then open the system prompt editor.

Step 4: Paste and customize the system prompt

Two sections to fill in before saving.

Personal details:

Replace four placeholders with your actual information:

[FIRST_NAME] and [LAST_NAME] as they appear in Apollo

[YOUR_EMAIL] which is your Apollo login email

[YOUR_USER_ID] from Step 2

Scoring model customization:

This part matters just as much as the personal details. There's a clearly marked "CUSTOMIZE FOR YOUR COMPANY" section with four fields:

Intent signal topics

What would your buyer Google right before they are ready to buy? Replace the placeholders with those topics. This is what drives the 30-point intent score.

Tech stack signals

What tools does your ICP currently use that signal fit or a displacement opportunity for your product?

Tier 1 personas

The job titles most likely to buy or champion your product. These contacts get surfaced first.

Tier 2 personas

Secondary titles that are relevant but not primary buyers. Claude only surfaces these if no Tier 1 contacts are available.

Firmographic fit

Set your ICP employee count range. Accounts that match get the full 10 points. Adjacent sizes get partial credit.

The more specific you are here, the better your account scores will be. If you're not sure, ask your manager or sales enablement team for your ICP definition.

How to use it every week

Open the project Monday morning. Claude automatically runs the full analysis: account discovery, enrichment, scoring, and contact lookup. It then presents a ranked table.

Each account shows:

• Score
• Key signals
• "Why now" reasoning
• Untouched verified contacts
• Suggested next action

You pick your next move:

A) Add contacts to a sequence

Claude matches the right sequence based on persona, industry, and intent, confirms with you, then enrolls directly.

B) Deep dive on one account

Full research brief including org chart, tech stack, recent news, talking points, and suggested messaging angle.

C) Draft a personalized email

Cold email written around the specific signal that triggered the account's ranking, with three subject line variations.

D) Re-run with different filters

Adjust geography, seniority priority, or intent topics and re-score.

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A few things worth knowing:

Claude pulls accounts using the Apollo analytics API filtered to a specific user ID, enriches the top 30 with a company search, then runs contact lookup only on the top 10 after scoring. So it is not burning API calls on accounts that will never make the cut.

The contact display is strict by design:
Only real people with a verified email who are not in an active sequence. Open job postings show up as scoring signals in the table, not as contacts. Learned this the hard way when an early version surfaced "VP of Sales (open role)" as an outreach target.

The new hire signal, meaning a new Tier 1 or Tier 2 exec hired in the last 90 days, is weighted at 15–20 points because those accounts usually have fresh budget conversations happening. It has consistently been one of the strongest signals during testing.

For teams with strict territory ownership or predefined account lists, this is a really practical way to prioritize within existing accounts instead of creating overlap or duplicate outbound across reps.

Happy to share the full system prompt if there's interest. Drop a comment.

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.