Lead List Builder

Someone on your team spends a full day building a list of 100 leads in a spreadsheet. They search LinkedIn, cross-reference company websites, guess at email formats, and manually check each one. Half the emails bounce. A quarter of the leads don't even fit your ICP. You just burned 8 hours to get maybe 25 usable leads.

Define your ICP once. Review the top-scored leads. Import to CRM.

1. You define your ICP: industry, company size, role, geography, signals. This is the only step that requires your brain. You tell the system who you want to sell to — what industries, how big, which roles, where they are, and what signals matter (hiring, funding, tech stack changes). You do this once.
2. GPT-4.1-mini scrapes and structures leads from public sources. LinkedIn profiles, business directories, databases, public listings. The model doesn't just grab names — it structures the data into clean, consistent records. Company name, title, location, all normalized.
3. Enrichment layer adds: company revenue, tech stack, recent news, contact info. Each lead gets fleshed out with real data. Revenue estimates, what tools they use, whether they just raised a round or made a key hire. Plus verified email addresses and phone numbers where available.
4. DeepSeek scores each lead against your ICP criteria. Every lead gets a score. DeepSeek is dirt cheap for this kind of classification work, so you can run thousands of leads through scoring without thinking about cost. High-fit leads float to the top. Bad fits get filtered out.
5. Ranked, deduplicated list pushed to CRM or exported as CSV. The final output is clean: no duplicates, no bad data, ranked by fit score. It lands in your CRM ready to work, or as a CSV if that's what you prefer. Your sales team starts the day with fresh, qualified leads instead of a research project.