Deepline is Claude Code for GTM. Here is the stack.

There's a quiet shift happening in how the best GTM teams are building their stacks, and most people are still pointing at Clay when you ask them what to use. Clay is a great product. We've written about why we think Clay is the undisputed champion of all GTM tools, and that piece still holds up. But the same week we shipped that, I started using a tool called Deepline for a couple of internal experiments, and the honest verdict after six weeks is this:

Deepline is what Claude Code did for software engineering, but for GTM.

That sentence sounds like a tagline, so let me unpack what it actually means and why it matters if you build outbound, ABM, customer success, or revenue operations for a living.

What Deepline actually is

Deepline (made by Aero AI Labs out of New York, backed by Lerer Hippeau and K5 Global) is a CLI and unified API for GTM data. You install it, plug in your provider keys, and you get one consistent surface across 95+ tools that historically sat in their own silos.

The product calls itself "GTM API designed for agents." That phrasing matters. The way Clay works is that you, the human, open the browser, design a table, drag waterfall steps, and click through configurations. The way Deepline works is that you, or your Claude Code agent, describe what you want in plain language and it runs across the right providers, in the right order, with automatic failover when one fails.

Concretely, the things it does out of the box are:

• Contact enrichment via waterfall across Apollo, Crustdata, Leadmagic, People Data Labs, Hunter, Icypeas, Prospeo, ZeroBounce, Forager
• Company enrichment with the same pattern
• Scraping via Apify, with a unified output schema
• Email validation across multiple providers
• TAM list building and ICP signal discovery
• Push to email sequencers (Instantly, Lemlist, HeyReach, Smartlead) in one command
• A PostgreSQL database for your data, in your own schema, with full export

And here's the part that turned my head: it ships as Claude Code skills, so you can drop slash commands into your Claude Code workflow and the agent knows how to call Deepline correctly. If you've used Claude Code to build software, you already know the shape of that workflow. It's the same shape, applied to revenue plumbing.

Why the "Claude Code for GTM" framing is earned, not generic

The thing that made Claude Code interesting wasn't that it could write code. Plenty of tools could already write code. The thing that made it work was that it could read your repo, understand your conventions, plan a multi-step change, and execute it across files without losing the plot.

Deepline does the equivalent for a GTM workspace. You describe an outcome ("get me 500 fit accounts for B2B SaaS with a recent VP of Sales hire and a job post for an SDR in the last 60 days"), and the agent plans the work across the right providers, executes the queries in the right order, validates the outputs, and writes the result to your Postgres or pushes it to Lemlist. The plumbing is invisible because it's an API designed for agents to call, not a UI designed for a human to click.

That's a different ergonomic from Clay. Clay is great if you, as a human GTM engineer, want to build a workflow visually and then run it. Deepline is great if you've already accepted that an agent is the thing doing the work, and you want one surface for the agent to reach 95 tools through.

For the work we do at Ziel Lab, where almost everything outbound is signal-triggered and AI-personalised, the second ergonomic wins. We've started building most new client workflows in this style.

The piece that completes it: Parallel.ai

Here's where it gets dangerous in a good way.

Deepline gives you a unified API for the data providers. But the providers are mostly built for known queries. You ask Apollo for a company by domain, you get a company. You ask People Data Labs for emails by LinkedIn URL, you get emails. Great. Useful.

What providers are bad at, even today, is open-ended research. "Find me the three biggest competitors of this company and how they describe themselves." "What's the public information about this account's M&A activity in the last 12 months?" "Pull every speaker at their last sales conference and their job changes since." That work used to be a junior SDR with a browser and three hours.

Parallel.ai is the piece that closes that gap. It's a web research API purpose-built for AI agents, with four modes (Research, Monitor, Extract, Search) and accuracy that beats Exa, Tavily, Perplexity and even GPT-5's native web search on the public benchmarks. It's SOC-II Type 2 certified, pay-per-query, and the outputs are verifiable with source citations on every atomic claim.

If Deepline is your unified provider layer, Parallel.ai is your unified research layer. Together they cover the two halves of "find the right accounts, then know enough about them to talk like a peer."

The architecture, end to end

Here's how we're running it for a Series B B2B SaaS we onboarded last month. The stack is intentionally lean.

The whole thing lives in a Claude Code project. We can read every step, replay it, change a prompt, and re-run on a slice of accounts in minutes. The Postgres layer means every result is queryable. Nothing is hidden in a vendor UI.

Five things you can do with this stack that you couldn't do cleanly before

1. Build a defensible TAM list from a vague ICP description

You describe your ICP in plain English ("US B2B SaaS, $5M to $50M ARR, sales-led motion, HubSpot users, no Clay yet"). Claude turns that into structured queries across the right providers via Deepline. Parallel.ai verifies the technographic and intent claims against the live web so you're not just trusting Crustdata's last refresh. You end up with a list that's both bigger and cleaner than what any single provider returns.

2. Run a research dossier per account at scale

This used to be the junior SDR job. Now Parallel.ai Research mode handles it, and Deepline merges the output into your Postgres alongside the structured firmographics. By the time an account hits an outreach trigger, your CRM record already has a 200-word brief on what's going on at the company, with sources you can click.

3. Stack signals across providers without writing glue code

Signal-based outreach is the only outbound that still works. We've been preaching this for a year. The hard part was always plumbing: connecting five providers, normalising their outputs, running them daily, deduping signals per account, persisting state. Deepline takes care of that plumbing layer. Parallel.ai Monitor runs the web-side checks. Claude Code calls both and writes the result to your schema.

4. Bring your own product data into the outbound layer

This is the under-priced one. Your product almost certainly knows which features the prospect's competitors are using. Or which integrations are on a free trial right now. Or what your most successful customers had in common at month 3. That data is locked in your warehouse and almost never makes it into the outbound message. Because Deepline runs in your environment with a Postgres of its own, you can join your product data to the Deepline output before the LLM writes the message. The message no longer says "I saw you raised a Series B." It says "I noticed three of your top accounts started using our X feature last week, and your team hasn't enabled it yet."

5. Run the same engine for Customer Success expansion

This is where things stop being about Sales. The same signal monitor that fires "this account is hiring an RFP manager" can fire "this customer just hired a Head of Data and their usage of our analytics feature is about to spike." Customer Success teams have been begging for this for a decade. It's two days of work in this stack. We've built it for two clients now.

What this looks like vs. a Clay-only stack

The Clay-only path still works. We're not telling clients to migrate today, especially if they've already invested in deep tables and complex waterfalls. What we are doing is this:

If you're still doing static one-off enrichments, Clay is fine. If you're building a daily signal-triggered outbound and expansion machine that runs while you sleep and you want to be able to read the code, the second column is where the future is going.

The honest caveats

This is the part where most blog posts gloss over the rough edges. We won't.

Deepline is young. The product launched in 2024 and the docs are catching up to the surface area. We've hit a couple of edges where a provider returned an unexpected schema and we had to write a thin wrapper. Their team is responsive but the bar isn't zero-config like Clay's polished UI.

Parallel.ai is fast but not free. Their pricing is per-query and they're honest about it. For high-volume monitoring you'll want to be smart about which accounts get the daily run versus the weekly run. We segment accounts by intent score and only run the expensive Research mode on the top decile.

The Claude Code muscle isn't free either. If you've never used Claude Code or agentic CLIs before, there's a learning curve. We bring it for clients as part of our AI automation work, but if you're trying to do this in-house and you've never written a slash command before, budget two weeks before you're productive.

This is not a CRM replacement. Your HubSpot or Salesforce still does the heavy lifting on pipeline management, attribution, reporting, deal stages. Deepline plus Parallel sits upstream and feeds clean, enriched, signal-rich records into the CRM. They are not a substitute for it.

Pricing reality

A practical breakdown of what this costs to run on a real account, for a team running 500 accounts through it monthly:

That's tooling at roughly €700 to €1,500 per month for a serious mid-market outbound + expansion machine, before sequencer and CRM. The historical equivalent built on Clay plus the same provider stack typically lands at €1,200 to €2,200 because of the Clay seat costs on top. The savings are real, but the real win is the agent-readable workflow, not the price tag.

Why this matters for Customer Success too

We've been talking about Sales because that's where this stack shines first. The same architecture pointed at a different question is what makes it work for Customer Success.

Sales asks "who should we reach out to and what should we say." Customer Success asks "which of our existing customers is at risk, who's expanding, and what's the right intervention." Both questions have the same shape under the hood: pull data on the entity, layer signals, decide on action, fire it through a channel.

When the same engine answers both, you get an organisation that runs on signal rather than gut feel, on both sides of the customer lifecycle. That's what we're building for clients today.

Frequently asked questions

Is Deepline a Clay replacement?

Not exactly. Clay is a tabular workflow tool with a beautiful UI. Deepline is an API and CLI built for AI agents. If your workflow is a human clicking through enrichments, Clay is better. If your workflow is an agent running queries on its own and you want every step in version control, Deepline is better. We use both for different clients today.

Do I need to know how to code to use Deepline?

You need to be comfortable in a terminal and willing to use Claude Code or a similar agent. You don't need to write Python or JavaScript. Claude writes the code, you describe the outcome. That's the whole point.

What about data residency and security?

Deepline runs in your environment with your Postgres. Your data stays in your schema. Parallel.ai is SOC-II Type 2 certified and you pay per query, so no data sits on their side beyond the request. For EU clients we host the Postgres ourselves in Frankfurt. We've shipped this for two German clients with strict BfDI requirements.

How is this different from Apollo plus Clay plus Outreach?

Apollo, Clay and Outreach are all UIs you click around in, each with their own data, their own logic, and their own provider integrations. Deepline plus Parallel.ai is one API layer that wraps all the providers, lets an agent run the work, and writes everything to your own database. Less UI, more output per hour.

Can you run this for us?

Yes. This stack is part of how we deliver go-to-market and AI automation work today. If you'd like a guided pilot where we set the architecture up for your team and hand it over after 90 days, book a call.

Will this still matter in two years?

Probably more. The trend is clear: GTM tools that ship as UI-first products are getting outmanoeuvred by ones that ship as agent-callable APIs. Deepline is early to that idea. We expect the rest of the category to follow. Better to be running the new stack now while everyone else is still designing Clay tables.