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In brief.
When a company evaluates an AI that drafts its answers to questionnaires and tenders, it looks at drafting speed. The real cost lies elsewhere: in verification. If every generated answer has to be reread and checked by an expert, the gain is limited to typing time: a few minutes, when verification takes fifteen. The equation only turns positive at the point where the system qualifies its own reliability, answer by answer, and enables targeted review.
The calculation, to run for yourself
Take a 200-question questionnaire, by way of illustration. Without AI: ten minutes per answer on average, research included — roughly 33 expert-hours. With a generic LLM: drafting falls to almost nothing, but every answer has to be verified, with sources to be found manually — say seven minutes per answer, 23 hours. Real gain: 30%, a long way from the promises, and carried by the same already-saturated experts. With a system that sources and scores every answer: 80% of high-scoring answers can be checked in one minute (the source is provided, it only has to be confirmed), while 20% concentrate expert attention. The same questionnaire falls to around eight to ten hours.
The evaluation criterion missing from comparisons
The exact figures vary; the structure of the calculation does not. It explains why so many generic AI rollouts disappoint despite enthusiastic users: time has moved from drafting to verification, without decreasing. At the demonstration, do not ask “show me a nice answer”, ask “show me how I will know which ones to check”.
The Optivalue.ai approach
This is Optivalue.ai’s architecture: every answer comes out with its source traced to document and page and a confidence score from 0 to 100, which turns exhaustive review into targeted review. The gain does not come from writing speed, it comes from measured confidence.
Why does the gain from generic LLMs disappoint?
Because time moves from drafting to verification instead of decreasing.
What is targeted review?
Concentrating human expertise on low-scoring answers and on gaps, validating the rest quickly.
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