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In brief.
How do you know whether you can trust an AI agent that prepares binding responses for the company? With a test bench: a set of real files whose correct answers have been established by experts, against which you run the system and count. Without that apparatus, nobody knows the tool’s reliability or how it evolves over time. Field feedback converges: internal pilots often plateau between 70% and 80% accuracy, a level that requires rereading everything and cancels out the gain.
The principle is simple, the execution is a discipline
Build a representative reference set: real files, with annexes, tables and ambiguous wording, annotated by experts. Define what you are counting: coverage (does the system find everything that is present), accuracy (is what it asserts correct), sourcing (does the cited reference genuinely support the statement). Replay that bench at every change of model, instruction or connector: this is regression testing, without which any modification is a leap in the dark.
The threshold that decides everything
Below roughly 95% accuracy, experts have to check every answer, because they do not know where the errors are hiding: the system saves typing time, not verification time, and the business case collapses. Crossing that threshold requires annotated data at volume, iterations with subject-matter experts and a permanent evaluation infrastructure. Hence the question that separates a demonstration from a production system: “show me your test bench and your curves over time”.
The Optivalue.ai approach
Optivalue.ai attaches a confidence score from 0 to 100 to every answer, backed by its sources, which makes review targeted instead of exhaustive: human attention concentrates on low scores and gaps.
Why 95% and not 80%?
At 80%, one answer in five is wrong without anyone knowing which: review has to remain exhaustive and the gain disappears.
What is a regression test?
Replaying the same bench after every change to verify that nothing has degraded.
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