Clay
Spreadsheet-like tool that pulls in data from 200+ sources and runs AI research agents on each row, so a sales team can build a fully-researched prospect list automatically instead of manually.
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Description
Building a genuinely good outbound prospect list means combining data from many separate sources — company info, funding news, hiring signals, contact details — and normally that means a person manually researching and copy-pasting between tabs for every single lead. Clay works like a spreadsheet where each row can trigger real research: an AI agent ("Claygent") looks things up across the web and 200+ connected data providers, filling in the row with exactly the information a sales team actually needs, at whatever scale a campaign requires.
Clay's "waterfall enrichment" automatically tries multiple data providers in sequence until it finds accurate information, its Claygents run AI-driven research per row, it tracks buying signals and intent data, syncs enriched audiences directly to ad platforms (LinkedIn, Meta, Google), includes a built-in email sequencer, and offers an API/CLI-accessible agent plugin for teams wanting to embed Clay's enrichment into their own systems. It's reached $100M in annual recurring revenue and a $5B valuation, with named customers spanning some of the most sophisticated AI companies (OpenAI, Anthropic, Mistral AI) as GTM tool users.
💬 Our review
The short version: Clay's adoption by OpenAI, Anthropic and Mistral AI as their own go-to-market tooling is a genuinely strong signal — these are companies that could build custom internal tooling if an off-the-shelf product didn't clearly save real time, and they're choosing Clay anyway.
"Waterfall enrichment" (automatically trying multiple data providers in sequence rather than betting everything on one source) is the practical feature that matters most for data quality — any single enrichment provider has gaps and inaccuracies, and stacking several sources with automatic fallback produces meaningfully more complete, accurate prospect data than relying on one vendor. The spreadsheet-native interface (versus a rigid, pre-built workflow tool) means a GTM team can build genuinely custom research and enrichment logic without engineering help, which is the real reason it's become popular beyond traditional sales-ops users. The honest caveat: pricing is credit-based and can get expensive fast at real scale (each enrichment/research action consumes credits, roughly $0.05 each, on top of monthly action allowances), so a team should model expected usage carefully before committing to a tier, and its most-cited adoption figure (500,000+ GTM teams) is a company-reported number worth treating as directional. Against Common Room (which layers in community/product-usage signals specifically) and Correlated (which focuses narrowly on product-qualified-lead scoring), Clay's strength is the sheer breadth of its 200+ data-provider marketplace and flexible, spreadsheet-style workflow-building.
💰 Pricing
📊 Global score
🤖 AI-enriched data
Free: 500 actions/mo, 100 credits. Launch: $167+/mo (15,000 actions, 3,000 credits). Growth: $446+/mo (40,000 actions, 6,000 credits). Enterprise custom. ~10% off annual.
Pros
Adopted by sophisticated companies (OpenAI, Anthropic, Mistral AI) as their own GTM tooling
"Waterfall enrichment" stacks multiple data providers for more accurate results
Spreadsheet-native interface lets teams build custom research logic without engineering
200+ data-provider marketplace, broader than most single-purpose enrichment tools
Cons
Credit-based pricing can get expensive fast at real usage scale
Requires careful usage modeling to pick the right tier
500,000+ GTM teams figure is company-reported, treat as directional