Portals such as GeM and the central e-procurement system brought public buying online. But a tender committee still has to read the tender, check each bid against it and build a comparative statement — often from scanned price schedules — under the knowledge that vigilance, audit and losing bidders may examine every line.
Two document problems
The first is the tender itself: eligibility, technical and commercial conditions spread across long documents. The second is the bids: vendor details and line items, prices and quantities, in whatever format each bidder chose.
Builds we reuse for tender evaluation
From our public-sector case study
| Build | What it does |
|---|---|
| Tender & RFP Intelligence | Document analyst, requirement extractor, compliance checker, researcher, writer, quality reviewer and risk assessor under an orchestrator |
| Bid Extractor | Bid PDFs and scans to line items, each value linked to its page, with chat over the bid |
Note: Built for industrial clients; reused for public procurement.
Source: DaasLabs, “Public-sector case study: from case files and spreadsheets to a decision record” (2026)
Why page links matter
A model that is right most of the time is not good enough on its own for a public award. What makes extraction usable is that every value can be checked in a click: the evaluator sees the number, the page and the passage, and confirms it. That turns AI from a black box into a faster way of doing the check the committee would do anyway.
If an evaluator cannot point to the page a number came from, it does not belong in a comparative statement.