AI-Powered RFP and RFQ Matching

Manually reviewing incoming RFPs and RFQs was slow, inconsistent, and couldn’t scale to meet growing procurement volumes. Eastgate built an ML-powered matching engine that automates document analysis and scoring for government and enterprise bid opportunities.

Challenge

• Each RFP or RFQ document required hours of expert analysis across technical, financial, and compliance dimensions
• Different evaluators applied criteria differently, leading to subjective outcomes
• Growing RFP and RFQ volumes outpaced the team’s capacity to review thoroughly
• Slow turnaround meant relevant RFPs and RFQs were identified too late to bid

Solution

• NLP pipeline to extract structured data from unstructured RFP and RFQ documents (PDFs, Word, web portals)
• Multi-criteria scoring model trained on historical RFP and RFQ data to rank relevance and fit
• Automated categorization by industry, region, contract type, and technical requirements
• Real-time dashboard showing matched RFPs and RFQs with confidence scores and automated notifications
• Human-in-the-loop validation to continuously improve matching accuracy

Architecture

Outcome

• 40% faster RFP and RFQ evaluation compared to manual process
• Consistent scoring across all evaluators through standardized ML criteria
• Automated processing of 500+ RFP and RFQ documents per month
• Higher bid accuracy - better matching leads to more competitive proposals
• Delivered in 6 months, iterating with user feedback from month 2

Tech Stack

• Backend: Node.js, PostgreSQL, Redis
• Frontend: React, TypeScript
• AI/ML: Python, scikit-learn, spaCy, sentence-transformers, RAG pipeline
• Infrastructure: AWS (ECS, S3, Lambda), Terraform
• CI/CD: GitHub Actions, Automated testing