Six weeks ago, I built a custom sequence analytics reporting interface I could deploy to anybody at our company in about 30 minutes, for about $30 in token costs.
\nI was reflecting on what this used to cost when I was running sales and GTM Ops teams. I’d have to hire a RevOps analyst and data engineer to hack together a curated set of reportable tables by connecting the CRM, the sequencing software, and our data warehouse just to get the data right. We'd also have to hook it up to a data viz tool like Tableau online, and then get the final output I wanted, an accurate reporting interface.
\nThat meant hiring the right team, buying a ton of software, getting data plumbed correctly, and having a data hosting solution. An expensive team, expensive software, time, and prioritizing. I won’t even get into maintenance costs.
\nThat's just one example of how things have changed. And it's the reason I’m passionate about making Apollo's developer surface a first-class product.
\nHere's how it works, and why I think the builders who get this right are the ones who'll be operating at a different scale than everyone else.
\nThe popular frame for AI in GTM is \"AI makes your workflow faster.\" That’s true, but it misses the structural point.
\nEvery historically sophisticated revenue team runs approximately the same stack. There are multiple systems of record, tools that were procured through different company eras, data challenges, and so on.
\nWhat do businesses really want? Insights, valuable information, and guidance about what’s working and what isn’t. Businesses want to grow and get better, but this setup gets in the way.
\nFirst, core systems are purchased. Hubspot, Marketo, Salesforce, Sequencing, Dialing, Quoting, to name a few. They all have different data models and house different data types.
\nEnter your analytics team. They pull all of the data from these core systems, procure a data warehouse, and land the data there. Build out your ETL layer to ensure you have standardized reportable tables. Standardized reporting gets produced based on that layer. Finally, ad-hoc requests begin to pour in because the foundation to get answers accurately and quickly actually exists.
\nBuilding this is expensive, driven primarily by recruiting and hiring people with the expertise and software cost.
\nWhat's changed is where intelligence enters that stack. Now that infrastructure already exists inside Apollo. When I built that dashboard, I didn't set up a data warehouse or wire up ETL pipelines. I just told Apollo what I wanted and got it in 30 minutes. No system pain, no big team, no alignment meetings on definitions. A stable data model that worked out of the gate giving me what I needed.
\nThis is possible because Apollo already has the plumbing: data, intelligence, and execution in one place, and our new developer surface is how any agent or workflow taps directly into it.
\nToday, we're giving this new developer surface a real home at apollo.io/developers. It ships with three ways to build on Apollo, what we call Headless GTM:
\nThe API is the foundation. It's always been there, but we've made it far easier to build against. We published a public OpenAPI spec that's machine-readable and CI-synced. Based on our initial analysis, agents are now completing multi-step tasks with up to 47% fewer tokens than from web docs. Hand it to Claude Code and it can write the integration for you. We also published an llms.txt index, so every endpoint is directly consumable by AI coding agents.
\nThe CLI is for production workflows. Headless, scheduled, agentic. No UI, no session state. When I built that sequence analytics dashboard, I was effectively deploying company-wide infrastructure. What did I use? CLI. Once you've got a workflow you trust, this is how you deploy it to run on its own.
\nAnd the MCP now comes in two forms:
\nNative integrations: We already have Apollo live in Claude, Codex, and Perplexity. Today we're adding Cursor, Replit, GitHub Copilot, and n8n. Apollo shows up directly in the marketplace of each, where developers are already building. Nothing to configure.
\nStandalone MCP: If you're working in a tool we don't have a native listing for yet, you're not blocked. Point to https://mcp.apollo.io/mcp from any MCP-compatible client and you're connected to Apollo's full data surface, no formal integration required.
All three plug into the same data, intelligence, and execution loop. But what determines how far you can take any of them comes down to one thing: context.
\nBoris Cherny, who created Claude Code at Anthropic, posted a framework in late June that got 3M+ views.
\nHe mapped out 5 archetypes for how roles are evolving on AI-powered teams: prototyper, builder, sweeper, grower, maintainer. His take was that these archetypes are cutting across traditional job functions. For example, an engineer, a designer, and a PM might all fit the same archetype depending on how they work.
\nI think he's right. And the reason those archetypes are blurring is that AI rewards context over credentials. The model and the prompt matter less than what you're feeding in. The people who move fastest are the ones who show up with the best context. Why did the sequence reporting layer get built so quickly? I knew what I was looking to build and was able to generate it in half an hour. I had context on the output I wanted, I just needed the right inputs; Apollo has them all.
\nFor GTM, that foundational “input” context is contacts, companies, signals, outbound history, and analytics. The full picture of your market and what you've tried. Every email where you earned a positive reply, every proposal a customer passed on... that's what Apollo's developer surface makes programmable. Every workflow you run informs the next one and the system gets smarter with every new action.
\nThe interesting question is who's consuming that context: builders or agents. For most, it's both. Our builders are composing workflows in Claude Code or Cursor and getting the \"$30 and 30 minutes\" version of something that used to require a team. Then we also have agents running \"next best action\" loops that score, prioritize, and surface the right actions for our reps. Both work because the context is already there.
\nAs those workflows get more sophisticated, the quality of GTM context they're operating with is what separates a good one from a generic one. That's the Apollo bet.
\n"}},{"type":"shareable_quote","fields":{"quote":"Everyone else is shipping AI with more limitations. Apollo shipped theirs with fewer. I build my own agents directly on Apollo's API, no UI, no guardrails, and they beat any off-the-shelf AI SDR.","name":"Simon Ooley","title":"Co-Founder & CEO @ Veles"}},{"type":"content","fields":{"content":"The builders who figure out how to give their AI the right GTM context, with the execution layer to act on it and a system that learns from every action, are the ones who'll be operating at a different scale than everyone else in two years. That is the new standard for \"world-class GTM.\"
\nWe're experiencing record revenue growth and got there by doing this, not just talking about it.
\nApollo has always been where GTM gets done. Now it's also where it gets built.
\nVisit apollo.io/developers, get an API key, and run one workflow before you close the tab… then let me know what you build with it.
"}}],"footer_cta":[]}},"customerStoryArticle":{"slug":"accord-customer-story","name":"How Accord Turned Stakeholder Mapping Into a Built-In Sales Motion","published":"2026-04-30T21:46:22.567836Z","updated":"2026-05-05T18:52:31.334773Z","scheduled":null,"status":"published","page_type":"magazine_article","fields":{"metadata":{"seo_title":"How Accord Scaled Multithreading with Apollo’s Data API","seo_description":"Discover how Accord embedded Apollo’s data API to automate stakeholder mapping, improve deal coverage, and help sales teams multithread faster — without manual research.","canonical_url":"","noindex":false,"authors":[{"meta":{"id":576249},"name":"Michelle Drennan","slug":"/michelle-drennan","description":"","headshot":"","job_title":"","linkedin":""}],"category":{"meta":{"id":631356},"name":"Customer Stories","slug":"customer-stories"},"categories":[{"meta":{"id":631356},"name":"Customer Stories","slug":"customer-stories"},{"meta":{"id":843756},"name":"Deal Management","slug":"deal-management"},{"meta":{"id":843757},"name":"Data Enrichment","slug":"data-enrichment"}],"tags":[{"meta":{"id":524586},"name":"Customer Stories","slug":"customer-stories"},{"meta":{"id":999892},"name":"Customer Stories","slug":"customer-stories"},{"meta":{"id":999966},"name":"Outbound Sales","slug":"outbound-sales"},{"meta":{"id":524592},"name":"Level 2","slug":"level-2"},{"meta":{"id":999961},"name":"Data Enrichment","slug":"data-enrichment"}],"published":"2026-03-18T00:00:00","content_updated_date":"","read_time":5,"hide_sticky":false,"toc_button_url":"","toc_button_text":"","new_tags":[],"is_editorial":false},"hero":{"background_color":"","featured_image":"https://cdn.buttercms.com/NvWN62bFQTefNSQNx8TV","summary":"What if stakeholder mapping didn’t require hours of manual research? See how Accord uses Apollo to help reps multithread every deal, faster.","hide_image_in_hero":false},"hero_cta_banner":[],"success_metrics":{"background_color":"","stats":[]},"body":[{"type":"content","fields":{"content":"Enterprise reps know they should multithread. The problem is, doing it well usually means hours of digging.
\nFor Accord, that gap was hard to ignore. Their Revenue Excellence Platform helps B2B sales teams operationalize value selling within the deal itself, so it actually gets used, not forgotten in a slide deck. But one of the most important parts of enterprise selling, stakeholder mapping, still required too much manual effort.
\nBy embedding Apollo’s data API directly into its platform, Accord made it easier for reps to find the right people and build stronger deal coverage — without leaving the workflow.
\nAccord is built around a simple idea: sales teams already know what great execution looks like. The challenge is making that behavior consistent across every deal.
\nIn enterprise sales, that often comes down to multithreading — identifying the right stakeholders and building relationships across the account.
\nBefore Apollo, that work was still largely manual. Reps would jump between LinkedIn, company websites, and other tools, trying to figure out who mattered and how they fit into the deal.
\nIt worked, but it was slow. And more importantly, it wasn’t consistent. When something takes extra effort, it’s easy to skip.
\nAccord knew this wasn’t the kind of problem it wanted to solve from scratch.
\nThe team evaluated external vendors with a clear focus: speed, data quality, and how easily their engineers could get up and running.
\nApollo stood out quickly.
"}},{"type":"shareable_quote","fields":{"quote":"The documentation compared to other vendors we looked at was really good. The team could get up and running very fast.","name":"Divya Chittoor","title":"VP of Product, Accord"}},{"type":"content","fields":{"content":"Apollo also delivered where it mattered most for the end user. During testing, Accord saw strong stakeholder matching, better coverage, and more complete profiles — including something especially important for their product: profile pictures.
\nFor a visual stakeholder mapping experience, that detail makes a difference. Reps aren’t just looking at names, they’re building a mental model of the account.
\nToday, Apollo helps power stakeholder recommendations directly inside Accord’s platform.
\nInstead of leaving the product to do research, reps can identify relevant stakeholders, add them to the map, and build out their understanding of the account in one place. From there, they can track influence, sentiment, and notes as the deal evolves.
\nThat shift, from manual research to embedded recommendations, is what makes the feature work.
\n“The easier you make these activities, the fewer excuses there are,” says Chittoor.
\nStakeholder mapping is a core part of how Accord helps teams execute better deals. Making it easier to use means more reps actually do it — not just the top performers.
\nWith Apollo embedded into the experience, stakeholder discovery no longer feels like extra work.
\nReps don’t have to carve out time for research. The information shows up where they’re already working. That alone makes the feature more valuable in both demos and day-to-day use.
\nAs for customers? They love it too.
\n“We’ve moved from aspirational multi-threading to actual multi-threading with Accord Stakeholder Maps and Stakeholder Recommendations,\" says Bayley Fesler, Director of RevOps at Xactly.
\nFor Accord, the benefits weren’t just on the user side. The integration itself was quick to implement, allowing the team to stay focused on the product experience.
\nAccord set out to make great sales behavior easier to execute, not just easier to define. By embedding Apollo directly into Accord, they’ve made stakeholder intelligence something reps actually use, not something they have to go find.
\nAs Ross Rich, CEO of Accord puts it:
\n“Whether it's creating Account Plans, driving deals forward, or post sales renewals / expansion; every stakeholder is identified immediately so sellers can multi-thread and proactively engage consistently. No more skipping key personas, tab switching, or detective work across LinkedIn, CRM, and separate systems. Just reps who are recommended exactly who to engage, when, on every deal & account. Apollo's technology and data made this easy to unlock.”
\n"}},{"type":"sign_up_demo_ctas","fields":{"title":"Turn data into deals today"}}],"footer_cta":[]}}}