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Proof and value

Can the AI be trusted, and is it worth it?

Two questions anyone paying for this will ask. The answers below come from a method the real pilot will repeat on a customer's own records.

Illustrative data. The 240 past records below are fictional and generated, so the figures show how the evidence will look, not how accurate the real AI will be. Real accuracy is measured in the shadow-mode pilot (Phase D).

Shadow mode: the AI against an expert, on past records

In shadow mode the AI grades records alongside the people who grade them today, and nothing it says is acted on. The two are compared. That comparison is the evidence that decides whether the AI is allowed to clear anything on its own.

95%
agree exactly with the expert
100%
within one risk level
70%
cleared with no person needed
1 of 167
cleared by the AI that the expert would have reviewed

Where they agree and disagree

AI’s risk level
Human expert’s risk level (rows) against the AI’s (columns), number of cases
1234
189400
207740
301431
400219

The diagonal is agreement. Above it the AI was more cautious than the expert (9 cases). Below it the AI under-called the risk (3 cases), which is the kind that matters most.

Agreement by process stage

  • Research & Development100% of 21
  • Manufacturing98% of 86
  • Packaging90% of 39
  • Distribution94% of 35
  • Quality System93% of 59

A stage that agrees less is where the pilot should look first and where the rules should stay strictest.

The disagreements, worst first

The platform is judged by its misses, not its average. A safe system keeps the dangerous ones rare, and the rules catch most of the rest.

CaseStageExpertAIAI confidenceWhat happened
HIST-0106ManufacturingC3B283%False clear AI under-called the risk and it was cleared without a human: a false clear
HIST-0113Quality SystemD4C365%Caught by the rules AI under-called the risk, but the rules still sent it to a human
HIST-0204PackagingD4C364%Caught by the rules AI under-called the risk, but the rules still sent it to a human
HIST-0036PackagingB2C357%Over-cautious AI more cautious: it asked for more review than the expert did
HIST-0095PackagingC3D464%Over-cautious AI more cautious: it asked for more review than the expert did
HIST-0158Quality SystemA1B269%Over-cautious AI more cautious: it asked for more review than the expert did
HIST-0185Quality SystemC2D376%Over-cautious AI more cautious: it asked for more review than the expert did
HIST-0187DistributionB2C358%Over-cautious AI more cautious: it asked for more review than the expert did
HIST-0207Quality SystemB1C274%Over-cautious AI more cautious: it asked for more review than the expert did
HIST-0216ManufacturingB2C361%Over-cautious AI more cautious: it asked for more review than the expert did
HIST-0220DistributionA1B272%Over-cautious AI more cautious: it asked for more review than the expert did
HIST-0232PackagingA1B273%Over-cautious AI more cautious: it asked for more review than the expert did

What it is worth

Move the assumptions to match a real team. The one measured input, the share the AI handles alone, starts from this live demo (50% of the 14 records currently in the system).

600 records
20 min each
12 min each
50 %
This demo measured 50%. Real figures come from the pilot.
10 %
130hours of review saved each month
65%less review time
0.9full-time equivalents released
300records a person still reviews

A model, not a measurement. It multiplies the inputs on the left, nothing more, and every input is an assumption to be replaced with measured values in the pilot.