Venture Capital Scout

The Problem Early-stage VC associates see over 200 cold inbound companies per month. Manually triaging each one—hunting down founder pedigree on LinkedIn, traction signals on GitHub, and recent press on Google—takes 30 to 60 minutes per startup. This tedious manual research burns hours of time that should be spent on core investment thesis work. The Solution VC Scout compresses half an hour of research into 90 seconds. Built without fragile agent frameworks, it accepts a company name and returns a structured investor brief, complete with a recommendation (take_call, dig_deeper, or pass), funding history, and automated diligence questions. How We Built It The engine relies heavily on Bright Data for live web intelligence. Instead of relying on an LLM's stale, pre-trained memory, VC Scout spawns a parallel collector registry. It uses the SERP API to pull recent news and context, the Scraping Browser to extract product claims from company sites, the Web Scraper API for deep founder pedigree, and the Web Unlocker for adaptive routing (e.g., checking GitHub for dev-tools). All gathered signals are bundled and sent to the AI/ML API (gpt-4o-mini). We use strict Pydantic structured outputs and a rigid system prompt that forces the model to cite specific URLs for every claim, ensuring absolute zero hallucination. If a company is in stealth, VC Scout honestly reports an empty footprint rather than making up facts.