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In brief.
More and more companies are building their own AI agent: an assistant assembled in-house on top of a market model, connected to the company’s documents, to analyse tenders and prepare responses. In a demonstration this works, and increasingly well. The real question is no longer capability, it is industrialisation: guaranteeing the completeness of the analysis, measuring accuracy, governing access, tracing approvals and maintaining all of it over time.
What has genuinely changed
Agentic platforms make it possible to assemble, in a matter of days, an assistant connected to the company’s documents and able to chain together file analysis, search, drafting and formatting. Many organisations therefore have an internal prototype, and it is impressive. Denying that would destroy any credibility; that is not the point.
What “end to end” conceals
A list of requirements that are invisible in a demonstration. Completeness: how do you know the agent has extracted every requirement, annexes included? Measurement: what accuracy rate, against which test set, revalidated how often? Access: does the agent respect each user’s permissions? Traceability: who approved which answer, against which source, on what date? Stability: does the agent behave the same way from one quarter to the next, while the underlying models change? Liability: who answers for an error in a signed document? A prototype answers none of these questions; a production system has to answer all of them. The distance between the two is a software vendor’s craft: evaluation benches, regression testing, permission partitioning, logging, support, contractual commitments.
The Optivalue.ai approach
Optivalue.ai is an agentic architecture, but an industrialised one: extraction with coverage measurement, per-answer confidence scores, page-level sources, approval workflow, full logging and contractual commitments. The question is not agent or no agent: it is who carries the harness in production.
Is an in-house AI agent a bad idea?
No, it is an excellent learning prototype. It becomes risky the moment its outputs bind the company.
Which question separates a demo from production?
“What is your accuracy rate, measured against which test bench?”
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