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Project context

Tender Drafting from Internal Knowledge

AI tender workflow that turns an RFP into a structured first draft by matching requirements to internal case studies, CVs, and reusable proposal knowledge.

Validated conceptMid-sized consulting firm2025

Client identity kept confidential

Headline result
3.8/5
Draft quality score
Measured
3.8/5
Draft quality score
Measured
~12
Minutes per first draft
Measured
5
Tenders tested
Observed

The challenge

The solution

Technology stack

LangGraphQdrantAzure OpenAIPython

The result

Bid managers rated the generated drafts as a usable starting point rather than a novelty output: 3.8/5 on average versus 4.5/5 for the original manually written winning submissions. First drafts arrived in around 12 minutes instead of 2 to 3 days, and the retrieval layer selected the right supporting material in 4 out of 5 tenders without manual correction.

That changes the economics of tender work. Less time disappears into reconstruction, and more time stays available for sharpening positioning, proof, and win strategy.

Next step

Want to explore where this could land first in your operation?

Start with one concrete workflow. We assess the first agent, platform fit, and the most sensible route to production.

Tender Drafting from Internal Knowledge Case Study | Laava