Independent AI VenturesAs a consultant, I was brought in as the primary technical contributor for an early-stage startup building an MVP that brings AI-powered analysis tools to small private equity shops. I took over technical ownership from a product-focused founder who had built an initial "vibe coded" proof of concept, and was tasked with transforming it into a hardened, shippable product. My core mandate was to design and rebuild the document ingestion and knowledge base architecture from the ground up, replacing a fragile prototype with production-grade infrastructure.
I redesigned the ingestion pipeline to chunk and embed incoming documents while tagging every extracted fact with a pointer back to its exact source page and paragraph, ensuring full provenance for every piece of information in the system. On top of this, I introduced a versioned knowledge graph that updated incrementally as new documents arrived, and built agentic tools that could cite sources for every claim they generated—giving PE analysts fast, trustworthy analysis with traceable evidence. I architected the system as an orchestrated processing pipeline with queue-based checkpoints, enabling seamless reprocessing with minimal repeated work whenever intermittent failures occurred.
The impact was substantial: ingestion reliability moved from requiring constant manual fixes to near-zero-touch reprocessing, and document processing time dropped from hours to minutes. This transformation took the product from a shaky demo to a robust tool ready to put in front of pilot customers, directly enabling the startup's path toward real customer validation.
