Automate prospect research for outbound sales. Company data, funding, hiring signals, tech stack. Deterministic rules. No ML. No data leaves your machine.
Prospect research before outbound sales eats 3-4 hours per day.
Googling companies, checking LinkedIn profiles, reading recent posts, looking at hiring pages, finding verified emails, checking if they just raised funding. Rinse and repeat 50 times. Then writing a personalized email based on what you found.
Existing tools are expensive and complex.
Clay and PhantomBuster cost $50+/month and require setup. You need something that just outputs structured research data and gets out of the way.
Feed it a list of target companies. Get back structured research data:
Company name and domain.
Funding stage if detectable from public sources.
Hiring signals from careers page.
Recent news headlines (last 30 days).
Detected technologies from job postings.
Direct URLs to company profiles.
$ echo '{"companies":[{"name":"Acme Corp","domain":"acme.com"}]}' | python research.py
{"prospects": [{"name": "Acme Corp", "domain": "acme.com", "funding_stage": null, "open_roles": null, "recent_news": [], "tech_stack": [], "linkedin_url": "https://linkedin.com/company/acme-corp", "crunchbase_url": "https://crunchbase.com/organization/acme-corp"}]}
Deterministic web scraping + data aggregation. No model, no API call, no data leaves your machine.
An LLM doing prospect research is a black box you cannot audit.
When a tool says "this company raised Series B", you need to know why. A rule you can read is a rule you can trust, debug, and improve.
EUR49 one-time. Runs locally. No subscription, no cloud, no data collection.
Buy now - EUR49