Explorium has raised $127,000,000 to date. Their CEO, Omer Har, walked us through the entire stack that runs the company, from the ads that bring people in to the dashboards that tell them whether any of it worked.
What makes this stack worth studying is not the tool count. It is how few humans touch a lead before it reaches a rep, and how consistently one layer shows up in every single stage.
Here is the whole thing, stage by stage.
1. Inbound Marketing
Their paid campaigns run on Reddit, Google and LinkedIn Ads, with Claude generating the ad creatives. That last detail matters more than it looks. Creative production is usually the bottleneck that caps how many angles a paid team can test in a quarter.
Where they are further ahead than the rest of the category is GEO, the practice of optimising for the sources AI engines pull answers from.
Instead of chasing rankings alone, they target the places that get cited: G2, ColdIQ, Trustpilot, GitHub, LinkedIn, YouTube and Reddit. Claude Code produces that content at scale, which is what makes covering seven surfaces at once realistic for one team.
They also list their product on AI assistants directly through MCP listings on Claude, ChatGPT, Perplexity and Manus AI. When a buyer asks an assistant to find them data, the product is a candidate answer rather than a search result they have to click through to.
Their influencer and affiliate programs cover TikTok, Instagram, LinkedIn and X.
2. Cold Outreach
Their outbound starts with an intent layer rather than a list.
RB2B identifies who is on the website. Bombora spots which accounts are in-market before they arrive. Between them, the team knows both who showed up and who is researching the category quietly somewhere else.
The data itself comes from Explorium, and Vibe Prospecting is the chat interface their team runs campaigns from. lemlist powers the multichannel sequences, and Claude Code orchestrates the whole motion.
The detail worth stealing here has nothing to do with tooling. Every list they build gets built with the engine they sell, which makes their outbound a live demo of the product. The campaign and the proof are the same artifact.
We built our own version of that intent layer after running these signals for clients across hundreds of campaigns.
You can check which companies are showing buying signals in your space right now, for free:
Intent Signals Tool
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3. Lead Capture
The capture layer is deliberately unexotic. HubSpot forms sit on a WordPress site, with HubSpot handling attribution.
The part that is not standard: an AI agent qualifies every visitor before a rep ever sees them. The form is the same form everyone has. What happens in the ninety seconds after submission is where the difference sits.
4. Lead Processing
This is where the bulk of their engineering hours go, and it is the strongest section of the stack.
A lead arrives → Explorium enriches it → n8n and a Claude agent score it and set the MQL → Salesforce and LeanData route it → a Slack bot pings the rep.
Explorium then maps the rest of the account for ABM expansion, so a single inbound lead turns into a picture of the whole buying committee rather than one contact record.
No human touches a lead until it is ready. That is the actual output of those engineering hours, and it is why the rest of the stack can stay simple.
Account expansion of that kind starts by finding the companies that look like the ones already converting.
You can pull a list of lookalike companies from your best accounts here, for free:
Lookalike Finder Tool
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5. Deal Closing
An inbound agent built on Claude and lemlist handles first contact. Calendly takes scheduling. Gong records the calls.
Three tools for the entire sales stage. Compare that to the lead processing layer above and the priorities become obvious: they spent their complexity budget on getting the right lead to the right rep, then kept the closing motion light.
6. Delivery
Stripe handles payments and Databricks handles data delivery. For a company whose product is data, delivery is a product surface rather than an afterthought.
7. Business Intelligence
GA4, Mixpanel and PostHog cover analytics. Datadog handles monitoring. Microsoft Clarity records sessions. Peec AI tracks their visibility inside AI search, which closes the loop on the GEO work from stage one.
Claude Code queries all of it. That is the fourth stage where the same layer appears, after content production, campaign orchestration and lead scoring.
Identifying what a company runs is the first step to understanding why their funnel behaves the way it does.
You can look up the technologies behind any company here, for free:
Tech Stack Finder Tool
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8. What This Stack Gets Right
Read the seven stages together and three decisions stand out.
The first is that intent comes before volume. RB2B and Bombora run ahead of the sequences, so the list is a consequence of the signal rather than the starting point.
The second is that the heavy engineering sits in the middle of the funnel. Capture and closing are ordinary. Processing is not. Routing a lead correctly compounds across every campaign, while a fancier booking page does not.
The third is consolidation. Claude and Claude Code appear in content, outbound, scoring, closing and BI. One capability spread across five stages beats five point solutions that each solve a slice, and it is a large part of why a stack this broad stays operable.
The uncomfortable question for anyone auditing their own stack is which layer of theirs appears in five stages, and whether any layer earns that.
We built a report that scores a company's GTM setup against the patterns we see across hundreds of these teardowns.
You can generate one for your own company here, for free:
GTM Reports Tool
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