Most AI writing tools are optimised to produce fluent answers. That is useful — and also how weak bids get dressed up as strong ones.
A buyer does not award points for fluency alone. They score against the question, the specification, and the evidence you put on the page. If the headcount is missing, the case study is vague, or two answers contradict each other, a polished paragraph will not save you.
That is why ProposAI now includes Buyer Review: an LLM-as-judge step that reads completed answers as a strict procurement evaluator would, then helps you fix what it finds — without throwing away the work you already did.
What “LLM as judge” means here
In AI research, "LLM as a judge" means using a model to evaluate another model's output against a clear standard, rather than asking the same writer to grade its own homework.
In ProposAI, the drafting model helps you write. Buyer Review uses a stronger, separate judge model to critique. The roles are deliberately split: one creates, the other challenges.
The judge is not there to rewrite your voice. It is there to ask the awkward evaluator questions early:
- Did we answer every part of the requirement?
- Is the claim supported by evidence on the page?
- Would a buyer doubt our capacity, compliance or consistency?
- What would strengthen this answer with the smallest change?
What Buyer Review looks for
When you run a review on a proposal, ProposAI works through completed answers and returns a buyer-readiness signal — green, amber or red — plus a list of findings.
Each finding is specific. It names the concern, points to the evidence (or the gap), and recommends a fix. Typical issues include missing requirements, weak evidence, unsupported claims, inconsistencies across answers, compliance risk, tone problems and gaps against your win themes.
You can jump straight from a finding to the question it relates to, then choose to ignore it, resolve it yourself, or ask ProposAI to fix it for you.
Fix for Me — surgical, not a full rewrite
Teams often hesitate to "regenerate" an answer after they have invested time in it. Manual edits, carefully chosen wording and organisation-specific detail should not disappear because one sentence needs work.
Fix for Me is designed for that moment. It takes the answer you already have, plus the buyer finding, and makes the smallest effective edit. The goal is to address the concern while preserving structure, tone and the detail you added by hand.
If the finding cannot be fixed without inventing a fact — for example, the buyer asked for employee numbers and none are in the answer — ProposAI will not invent a headcount. It asks you for the missing detail, then applies the surgical edit once you provide it.
That is the difference between a writing assistant and a review process: the system is allowed to stop and ask, rather than paper over a gap with confident language.
Why this matters for bid teams
Award-stage packs fail late for familiar reasons: somebody assumed a fact was "obvious", two writers used different numbers, or a polished draft never faced a sceptical read before submission.
A buyer-lens review compresses that late panic into an earlier, calmer loop. Authors still own the answer. Reviewers still apply judgement. The AI judge simply makes gaps visible while there is still time to fix them.
It also keeps humans in the right place. You approve facts. You decide what to ignore. You resolve what the buyer will actually care about. The model does not submit the bid for you.
How to try it on a live proposal
Open a proposal with at least one completed draft answer, run Buyer Review from the proposal workspace, and walk the findings in severity order.
For each open item, ask: would a real evaluator raise this? If yes, use Fix for Me — and answer any fact prompts honestly. If no, ignore it and move on.
When the open high-severity list is clear, you are closer to a submission that can survive a critical read.