Led development of a B2B SaaS platform designed to accelerate RFP response writing for enterprise clients.
Key Contributions
- Architected and built dynamic RAG pipelines using GPT-4 and Gemini, paired with vector databases for context retrieval
- Reduced hallucination rate from 12% to under 0.5% through a multi-layer validation system
- Implemented BullMQ-based job queues for async document processing at scale
- Built a real-time collaborative editing interface for proposal teams
Technical Stack
The platform runs on Next.js with a Node.js backend, PostgreSQL for structured data, and Pinecone for vector storage. Document ingestion pipelines handle PDF, DOCX, and HTML formats, chunking content intelligently for retrieval.
Impact
The platform reduced average proposal writing time from 40 hours to 8 hours per RFP response, representing an 80% efficiency gain for client teams.