The Government e-Marketplace (GeM) carries a very large volume of bids across central and state procurement every year. For vendors, participation has long been a paperwork-heavy slog: searching, qualifying, drafting, submitting. AI is now reshaping each of those steps, but the impact is uneven. A lot of vendor effort still goes into work that AI handles better, while some teams are automating steps that should stay firmly with people.
Where AI is already moving the needle
- Tender discovery. Semantic search across daily GeM listings, scoring each bid against the vendor's capability profile, replaces hours of manual scanning with a ranked shortlist.
- Eligibility extraction. Vision-LLMs parse bid documents and ATC files into a structured eligibility checklist: turnover thresholds, past experience, certifications, EMD and delivery terms. A bid/no-bid decision that needed a long manual read becomes a short review of an extracted summary with page references.
- Drafting first-pass responses. RAG over the vendor's prior submissions, capability documents and case studies generates a first draft. People edit and approve. How much time this saves depends heavily on how well organised the source material is.
- Compliance validation. Pre-submission checks flag missing documents, figures that don't match between forms, expired certificates and non-conformant formats while fixes are still cheap.
Where the rules still rule
AI doesn't change the rules, only the cost of complying with them. Identity verification, digital signing, EMD and bank guarantee instruments, OEM authorisation chains and local-content declarations are still rules-bound, and a person signs for them. Don't expect AI to make ineligible vendors eligible. It makes eligible vendors faster.
There is also a newer risk: a model that drafts confidently can invent experience, certifications or figures. Every factual claim in a submission must trace back to a document you hold. False declarations can lead to action against the seller under GeM's terms, whoever or whatever drafted them.
A reference pipeline for vendors
- Collect: gather new bids and corrigenda daily through the portal's alerts and other permitted methods, within its terms of use.
- Score: compare each bid's category, value, location and requirements with your capability profile and rank the shortlist.
- Extract: turn shortlisted bid documents into a checklist with page citations.
- Decide: a person makes the bid/no-bid call using the checklist and a pricing view.
- Draft: retrieval-based drafting of technical responses from approved source material only.
- Check: automated completeness and consistency checks, then human sign-off.
The vendor playbook for 2026
- Maintain a continuously updated capability profile and past-performance library that an LLM can retrieve from: work orders, completion certificates, financials and certifications, each tagged with its expiry date. This is the single biggest investment.
- Run a daily discovery feed scored to your fit instead of scanning manually.
- Standardise your response format and let RAG draft from it, so reviewers focus on tailoring rather than blank-page authoring.
- Run the pre-submission compliance check well before the deadline, for example 48 hours ahead, so there is still time to obtain a missing certificate.
- Keep pricing decisions with the people who own the margin.
A worked example (hypothetical)
Say a mid-sized IT services firm tracks about 40 relevant GeM bids a week, and two analysts currently read every document to decide whether to bid. With an extraction step, each bid arrives as a one-page checklist: eligibility criteria, EMD, delivery timeline, notable ATC clauses and the pages they came from. Analysts spend their time on the bids that pass the screen and on pricing. Each week they spot-check a few extracted checklists against the source pages, so extraction errors are caught before they cost a bid.
What to automate and what to keep human
| Step | AI role | Human role |
|---|---|---|
| Discovery | Search, score, rank | Tune the profile, review the shortlist |
| Eligibility | Extract criteria with citations | Make the bid/no-bid decision |
| Drafting | First draft from approved sources | Edit and verify every claim |
| Pricing | Summarise your own past bids and outcomes | Set the price and the floor |
| Submission | Completeness checks | Sign, declare, submit |
Keep your own data safe
Bid documents are usually public; your pricing, margins, financial statements and client references are not. Run tender AI on business or enterprise plans whose terms exclude training on your data, or on infrastructure you control. Restrict the capability library to the people who need it, log who retrieved what, and keep pricing sheets out of general-purpose chat tools entirely.
The buyer-side picture
Procurement teams are beginning to explore AI on their side too, for drafting specifications, comparing submissions and analysing spend. Adoption there is slower and more cautious, as it should be with public money. For vendors the implication is simple: clear, well-structured and internally consistent submissions are easier for any reviewer, human or machine, to evaluate.
STQC, accessibility, and AI
If you respond to digital-services tenders, expect requirements such as STQC testing or certification and accessibility conformance under the Guidelines for Indian Government Websites, which align with WCAG 2.1 Level AA, in support of the Rights of Persons with Disabilities Act, 2016. Software written with AI assistance goes through the same audits as any other code. Plan the compliance pass into the project timeline; it is hard to retrofit.
How we approach this at Velura Labs
Our AI & Data Solutions service builds tender-intelligence systems for vendor-side and buyer-side use cases. For document extraction at scale, see Document Processing and Document Digitisation, and read our government digitisation guide for the broader public-sector context. Talk to us if your government business development team is drowning in RFPs.
Available to businesses across the United States (Washington, California, Texas, New York), Europe (France, Italy and the wider EU), the Middle East (Dubai and the Gulf) and India. Get in touch to scope your build.