Clay

Data enrichment and AI research agent for outbound sales and GTM teams

AI Marketing & E-commerceFree planOverseas★★★★☆ 4.0

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What is Clay?

Clay starts with a table of companies or people and enriches every row from a long list of data providers, falling back through several sources until a field is filled. On top of that plumbing sit AI research columns, where a model is given a prompt and web access to summarise a company, find a relevant signal or draft a personalised opening line per row. The result is an outbound workflow that is partly data engineering and partly language-model work: enrichment is provider lookups, and the agent research is genuinely generative. It suits go-to-market teams building targeted lists at volume.

Last updated: 2026-09-20. This site only provides an index; for exact features, pricing, and licensing, see the official website.

Key features

Waterfall enrichment that queries multiple providers per data point
AI research columns that summarise companies and find signals
Generated personalised opening lines written per row
Large integration library covering CRMs, sequencers and ad platforms
Table workspace with formulas, filters and bulk runs
Workflow triggers that push enriched rows to other tools

Pros & cons

Strengths

Waterfall enrichment fills far more fields than single-source tools
AI columns turn research into something you can run per row
Composes well with the sequencer you already use

Watch out for

Credit costs add up fast on large tables and repeated runs
Steep learning curve before the table logic clicks
Generated personalisation still needs review to avoid obvious misses

Best for & use cases

outbound prospecting, lead enrichment, personalised cold email and gtm research

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FAQ

Which parts of Clay use AI?

Two clearly different halves. Enrichment is data plumbing: it queries providers until a field is filled. The AI side is the research columns and the generated outreach lines, which use language models with web access.

Do I need to be technical?

A little. Building a table with formulas, filters and waterfall logic is closer to spreadsheet work than to coding, but the concepts take time. Non-technical sellers often learn it faster with a template.

How do credits work?

Enrichment lookups and AI runs both consume credits, and a big table with several providers per row can burn through an allowance quickly. Testing on a small sample before running the whole list is the usual habit.