AI RFP Manager — Intelligent tender management with a Go/No-Go decision engine
AI RFP Manager is an AI-powered platform that transforms how companies handle tenders and RFPs. The company feeds the system its institutional knowledge — past project history, delivery records, team capabilities, certifications, and pricing data — and the platform turns that knowledge into an always-ready bidding engine.
When a new tender arrives, the system automatically parses the RFP document, extracts requirements, eligibility criteria, and deadlines, and generates a tailored proposal draft grounded in the company’s real past work. The standout capability is the Go/No-Go engine: before a single hour is spent writing, it scores the tender against the company’s capabilities, capacity, past win patterns, and risk factors — and delivers a clear, evidence-backed verdict on whether the tender is worth pursuing.
As the backend developer, I designed the document ingestion and RAG pipeline, engineered the multi-factor Go/No-Go scoring engine, and built the asynchronous proposal generation workflows that assemble compliant, structured proposal drafts from the company’s knowledge base.
Core features
- Company Knowledge Base — ingest past projects, case studies, team profiles, certifications, and financials into a searchable vector index
- Tender Ingestion & Parsing — upload RFP documents (PDF, DOCX) and automatically extract scope, requirements, eligibility criteria, and deadlines
- Automated Proposal Generation — AI drafts a complete, structured proposal grounded in the company's real past work and win themes
- Compliance Matrix — every RFP requirement mapped to a proposal section, so nothing is missed before submission
- Deadline & Pipeline Tracking — dashboard of active tenders, statuses, and submission timelines
Platform capabilities
- Capability Fit — semantic matching of tender requirements against past delivered projects and in-house expertise
- Eligibility Screening — hard checks on certifications, registrations, turnover thresholds, and mandatory criteria
- Capacity & Timeline Analysis — evaluates current workload and delivery windows against the tender schedule
- Risk & Win-Probability Scoring — weighted score built from past win/loss patterns, competition signals, and contract risk factors
- Evidence-Backed Verdict — a clear Go / No-Go recommendation with the reasoning and supporting references, not just a number
Technical architecture
Challenges
- RFP documents arrive in wildly inconsistent formats, structures, and languages
- LLM-generated proposals drifting from the company's actual capabilities and past work
- Turning a subjective bid/no-bid judgment into a repeatable, explainable score
- Long-running AI generation blocking the API and degrading user experience
- Keeping the knowledge base fresh as new projects complete and teams change
Solutions
- Built a normalization pipeline that converts any RFP into a structured requirements schema before scoring
- Grounded every generated section with RAG over the knowledge base, with citations back to real projects
- Split the Go/No-Go engine into hard eligibility gates plus weighted capability, capacity, and risk scores
- Moved parsing, embedding, and generation to Celery workers with real-time progress updates
- Automated re-indexing workflows so completed projects flow straight into future proposals