Legal Bid Document Generation Pipeline

Problem

Legal bid documents often fail on two fronts: tiny template mismatches such as table columns, punctuation, or signature blanks, and fragmented technical or business content that is hard to reuse across cases. legal-bid-pipeline treats the work as a traceable pipeline: parse the tender file, retrieve company materials, draft sections, merge template and free-form content, and run QA before delivery.

How It Works

• Feasibility gate: screen qualifications, disqualification clauses, deadlines, and scoring weights before writing begins, producing a feasibility report.
• Tender decomposition: extract qualification, technical, commercial, scoring, and submission requirements; generate section briefs and a compliance matrix draft.
• Template detection and dual-track assembly: rule-based scans identify tables, signature blocks, declarations, and deviation tables. Template sections are filled word-for-word from the tender template; free-form sections are written against scoring criteria.
• Knowledge-base drafting: when ima-mcp is available, company profiles, licenses, case studies, and technical documents are retrieved first; otherwise the pipeline falls back to AI generation and lists content for review.
• QA and review loop: hard gates check formatting, template alignment, and risk-library hits, then five-dimensional review strengthens scoring gaps before DOCX/PDF assembly.

Boundaries

• A tender file is required; pricing, authorized representative, team roster, and bid/no-bid decision must be supplied by the user.
• AI-generated sections are drafts, not submission-ready content; formal bids should be reviewed line by line.
• Weak-context models can lose template rules or hallucinate facts, so use a strong instruction-following model and keep source assets separate from output files.