Manual Data Verification
A person confirms the value — the human check, used deliberately where it earns its cost.
Manual data verification is human confirmation of document-derived data — a person comparing the value against the source and attesting it's right. In the pre-automation world it was the process (enter, then verify, sometimes double-key); in an automated pipeline it becomes a deliberate instrument deployed where human judgment earns its cost: the low-confidence extraction, the high-stakes field, the regulated checkpoint, the sampled audit of confident output. The design question shifts from "how do we verify everything" to "which verifications, by whom, triggered how" — and the entries on review workflows and validation pipelines cover that architecture; this one concerns the verification act itself.
Doing the act well is interface and psychology. Effective verification shows value and source together (the field beside its highlighted page region — comparison at a glance), sequences attention by risk (uncertain and consequential fields first), and makes confirmation cheap but not free — friction calibrated so attestation means looking, not reflexive clicking. The known enemy is automation bias: verifiers presented with a plausible pre-filled value systematically under-catch its errors, and the effect strengthens with the model's accuracy — the better the system, the sleepier the checker. Countermeasures are structural: blind verification for the highest stakes (the human reads the source before seeing the model's answer), seeded known-errors measuring each verifier's catch rate, disagreement-rate monitoring (the verifier who never disagrees isn't verifying), and rotation that keeps eyes fresh.
Verification's output deserves the same respect as its act: each confirmation or correction recorded with verifier, timestamp, and basis — feeding the audit trail (who attested this value), the accuracy measurement (true production error rates come from exactly this data), and the training loop. The economic lens completes the picture: manual verification is the most expensive per-field operation in the pipeline, which is precisely why it should be spent like money — targeted, measured, and continuously re-aimed at wherever the current models' uncertainty actually lives.
The form fills itself; the human confirms — flipping data entry from typing to checking.
Four eyes on what matters — no single person (or single model) gets to be wrong alone.
The people, plumbed in properly — validation as an engineered stage, not an inbox.
Proof Perimeter runs document AI inside your own perimeter — with a provenance record on every field.
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