Tender Estimation Agent

An AI-powered workflow that turns messy tender packs into a structured estimate: requirements → scope → effort → risks → decision.

Context

Tender documents are usually a mix of PDFs, scans, tables, and “please read annex #7” links. Teams lose time on:

• searching for key requirements,
• comparing them with internal capabilities,
• preparing repetitive estimate documents,
• and making a go/no-go call without missing hidden constraints.

Problem

Manual tender analysis is slow and inconsistent:

• Critical requirements are easy to miss (deadlines, certifications, penalties).
• Different people produce different estimate formats.
• Valuable past learnings don’t carry over to new bids.

Approach

The agent is not “a chat”. It’s a pipeline with guardrails:

1. Ingest: upload PDFs/Docs/Spreadsheets and normalize into text + tables.
2. Index: chunk + embed, store in a vector database for retrieval.
3. Extract: requirements, deliverables, deadlines, constraints, evaluation criteria.
4. Estimate: draft a scope and effort outline using internal templates.
5. Risk: generate a risk register with mitigation actions.
6. Decision: go/no-go recommendation with explicit assumptions.

Implementation notes

• Retrieval uses “cite-first” prompting: every claim in the summary must reference a source chunk.
• A strict schema keeps output consistent (sections, checklists, tables).
• Red flags are elevated: missing pages, inconsistent dates, contradictory requirements.

Output (what the team gets)

• A one-page summary for stakeholders
• Requirements table (must-have / nice-to-have / blockers)
• Estimate draft (milestones + assumptions)
• Risk register
• Go/No-Go recommendation with confidence and reasons

Next steps (easy upgrades)

• Plug in historical bid outcomes to learn “what wins”
• Add evaluation / regression tests for common tender patterns
• Auto-generate clarifying questions for the customer