Enrich a lead list without a subscription

Export raw prospects from Apollo, lemlist or LinkedIn, enrich them with live research, and pipe them back into your sequencer.

Updated August 2026

What goes in, what comes out

Input

  • Full name
  • Company
  • LinkedIn URL

Output

  • Current title
  • Seniority
  • Company summary
  • Recent trigger event

The prompt

Paste this into the description field, or write your own version of it.

Find this person's current job title and seniority level, a one-line summary of their employer, and any notable company news from the last six months that would make a relevant outreach hook.

Why this works

The expensive part of outbound is not the contact record. It is the context that makes a message worth reading. Trigger events, recent launches and funding news are public, and a model with search finds them per-prospect at a fraction of a cent.

Getting the best results

This layer adds the context that makes a message land: the trigger event, the positioning, the reason to reach out now. Keep your existing contact source for verified emails and let OpenClay handle everything around them.

What it costs

On Gemini 3.1 Flash-Lite, a typical row of this shape costs about <$0.01, so roughly $0.27 per thousand rows, paid directly to Google. OpenClay adds nothing.

The tool shows an exact figure after a five-row test, before you commit to the batch. See all models and pricing.

Step by step

  1. 1Export your prospects. Pull the raw list out of Apollo, lemlist or wherever it lives, as CSV.
  2. 2Pick the identifying columns. Name plus company is usually enough; a LinkedIn URL improves accuracy.
  3. 3Describe the research. Ask for the specific angles you'd actually use in a message.
  4. 4Import back. Download the enriched CSV and load it into your sequencer as custom fields.

No account, no card, no platform fee.

Bring your own API key and pay the model provider directly.

Start enriching, free

Other use cases

  • Enrich a company list with AI: Turn a column of company names into leadership, funding, headcount and positioning, researched from the live web one row at a time.
  • Build a competitor matrix with AI: Give it a column of competitors and get pricing, positioning, target segment and differentiators back as structured columns.
  • Enrich a list of universities: Rankings, acceptance rates, tuition and programme details for hundreds of institutions, without copying from a dozen tabs.
  • Track hiring signals across companies: Open roles, the functions being hired, and the careers page URL: a decent proxy for where a company is investing.
  • Enrich any CSV with AI: The generic case. If you can describe the column you want in a sentence, and the answer exists on the public web, this works.