Bidvera

Responding to an enterprise or government RFP means reading hundreds of pages, finding every mandatory requirement, checking eligibility, writing tailored sections backed by real past work, pricing the bid and assembling a formal dossier, usually against a tight deadline. Missing one mandatory clause can disqualify the whole bid.

Single-prompt AI tools are too fragile for this: they skip requirements, invent credentials and cannot be paused for a go/no-go decision. Bidvera needed to be thorough, grounded in verified company facts, fast through parallelism, and controlled by the people who sign the bid.

Bidvera runs on a LangGraph StateGraph whose BidState holds bid metadata, requirements, the outline, pricing and the compliance matrix. Send-based fan-out runs requirement extraction across documents and drafting across sections in parallel. At the bid/no-bid milestone the graph calls interrupt(); an executive reviews and the run continues with Command(resume=...), with comments and decisions saved in durable checkpoints that survive restarts.

Tools live in four isolated FastMCP servers speaking JSON-RPC 2.0: tender-search for discovery, company-knowledge for verified case studies and CVs, pricing for rate-card calculations, and documents for final artifact generation. Drafts must cite company knowledge, and pricing is calculated deterministically. Documents are parsed with pdfplumber, PyPDF2 and Docling, and dossiers are produced with python-docx, including styled tables and compliance matrices, then converted to PDF.

The FastAPI backend streams graph execution over Server-Sent Events to a Next.js 16 and React 19 workspace with step inspection, qualification status and document previews. A floating copilot answers questions about SLAs and penalties with page citations, redrafts sections on request, and manages bids. A Pytest benchmark harness runs gold RFP datasets to check extraction recall, eligibility accuracy, hallucinations and prompt-injection resistance.