The problem
Drafting bid and RFP responses takes a long time. For the bid team at this UK-based franchise — a multi-site operation — every tender meant starting close to a blank page: pulling together case evidence, finding the right way to answer a question they had almost certainly answered before, and getting the wording to sound like the business. The knowledge existed. It was just locked up in a decade of past proposals that nobody could query quickly.
That is a common and expensive pattern. The cost is not only the hours; it is the bids that get a thinner response than they deserve because the clock ran out, and the institutional knowledge that walks out the door when an experienced bid writer moves on.
The constraints
This is a bid team's competitive edge, so the work is sensitive by nature. A few things shaped the build:
- Factual grounding, not invention. A bid response that confidently states something the company cannot deliver — or misquotes a past project — is worse than no response at all. Everything the system produces has to trace back to real information.
- Their voice, not a generic one. Procurement teams can tell when a response has been bulk-generated. The output had to read like this business, not like a chatbot.
- A decade of mixed-quality source material. The source data spans 10+ years of proposals, both the bids they won and the bids they lost. The losing bids matter as much as the winning ones — they tell you what not to repeat.
- A working tool for a working team. The bid team are the users. The interface had to fit how they already draft, not ask them to learn a new discipline.
What we built — Phase 1 (live)
We started by mining a deep data analysis of more than ten years of the client's bid proposals — successful and unsuccessful — and structuring it into a very efficient database that the system can search at speed.
On top of that sits Brian — an interface the bid team uses directly. They can query the whole history of the business's bidding and draft responses in the company's own tone of voice, grounded in actual factual information rather than fabricated or dreamt-up content. Because every answer is driven from real bid material, the team gets language that is both on-brand and defensible. This is the core of the bid response AI: not a generic writing assistant, but a system that knows how this business has answered these kinds of questions for a decade.
Phase 1 is live and delivering value today.
What we're building — Phase 2 (in build)
Phase 2 takes the same grounded foundation and points it at the front of the process. The bid team will upload a full bid pack, and the system tears it down: it extracts every individual RFP question, categorises them, and can then bulk-draft a first response to any of those questions using the decade of data behind it.
The aim is concrete. An RFP packet goes in, gets analysed, the team works through it, and out the other side comes a tender response that is 70–80% drafted and ready — which the bid team then refines, finalises and signs off. The system does the heavy lifting of the first pass; the humans keep judgement and ownership of what actually goes out.
Why it's built this way
A bid response is a promise to a customer, so the engineering choices all point one way: keep humans in control and keep the model honest.
Grounding everything in the client's own decade of bids is what stops this being a generic content generator. The model is not asked to know about the business in the abstract — it is asked to retrieve and reassemble what the business has genuinely said and done. That is what makes the tone-of-voice match real rather than imitated, and it is what lets a bid writer trust an answer enough to build on it.
Keeping both won and lost bids in the source data is a deliberate call. The losing proposals are training in disguise — evidence of what didn't land — and throwing them away would throw away half the lesson.
And the 70–80% target in Phase 2 is set where it is on purpose. We are not trying to remove the bid team; we are trying to get them most of the way down the track, fast, and leave the final judgement — the parts that win or lose the work — with the people who own the relationship.
The outcome so far
The clearest signal has come from the top of the commercial function. The client's Commercial Director gave feedback that what he was able to do with the system, simply by querying it, saved him days of time.
Phase 1 is live and in daily use; Phase 2 is in build. The trajectory is a bid team that spends its time on the parts of a tender that actually decide the result, instead of on rebuilding answers it has already written a dozen times before.
“Lumitec helped us unlock years of bid knowledge and turn it into something our teams can use at speed.”
— Commercial Director, national franchise group
Where this goes next
The same pattern — ground a model in an organisation's own history, keep people in control of the final word — applies well beyond bids. Any team that answers the same hard questions repeatedly, from a deep well of past work, can be sped up the same way.
If your team is drowning in proposals, tenders, or any high-stakes document work, the place to start is our free, no-obligation AI Readiness Diagnostic. We learn how you work, map where AI would actually pay off, and give you a statement of work, a wireframe and a clear ROI case — at no cost, with no commitment to build.
“Lumitec helped us unlock years of bid knowledge and turn it into something our teams can use at speed.”
— Commercial Director, national franchise group