The task
Teams often need a few reliable fields from a large collection of inconsistent documents. Configure an agent to extract a defined schema, retain a reference to the original evidence and separate incomplete records for review. In this playbook, equipment maintenance forms become an operational register with inspection dates and missing-field flags.
What you provide
Representative documents
Provide equipment maintenance forms, including different layouts, poor scans and records with missing fields.
Field definition
For example: equipment ID, service date, technician, next inspection date and source reference. Define allowed date formats and required fields.
Validation rules
Specify how to handle missing values, duplicate records, ambiguous dates and mismatched equipment identifiers.
Give the agent a clear brief
Use these instructions as a starting point, alongside your approved sources and permissions.
Extract the requested maintenance fields from each document. Return one row per service record with a source document and page reference. Preserve the original value alongside any normalized date. Use not stated for missing fields and needs review for ambiguous values. Do not invent inspection dates or merge conflicting records. Produce a reviewable table using the supplied schema.
How the work comes together
Read against a defined schema
Process text or OCR from the permitted documents and extract only the requested fields. Avoid collecting extra personal or operational data that the task does not need.
Validate and preserve uncertainty
Apply the field rules and keep the original source values. Flag unclear dates, duplicate IDs and missing fields rather than silently filling gaps.
Return rows with traceable evidence
Write the results into a structured table with source references and a review status. A reviewer can check the flagged rows before the data enters another system.
Illustrative example · fictional data
A maintenance register that keeps the gaps
Three fictional forms contain a complete inspection date, a missing date and an ambiguous date. The schema requires an unambiguous inspection date.
| Equipment | Next inspection | Evidence and status |
|---|---|---|
| Pump P-017 | 2026-12-15 | Form A, p1 · ready for review |
| Valve V-022 | Not stated | Form B, p2 · missing field |
| Motor M-009 | Needs review: 04/05/2027 | Form C, p1 · date format unclear |
An empty or uncertain field is a useful result. The reviewer can resolve it against the original document instead of correcting an invented value.
The human review
A document specialist checks uncertain values and confirms the destination schema. Import into a maintenance or other operational system requires a configured connection and its normal validation process.
Set it up for your team
Define fields before extraction
Supply field names, meanings, required values and examples. Decide which values can be normalized and which must remain as written.
Test the document mix
Include real layout variation, handwriting if relevant and poor scans. Agree on a review policy for extraction failures.
Choose an output destination
Use a structured table first. Confirm export format and field mapping before connecting downstream systems.
Check the result before scaling
Compare extracted fields with a human-reviewed sample. Measure correctness by field, missing-value handling, source-reference accuracy and review effort across the document types you actually receive.
Keep the data in the right place
Operational documents may expose equipment, locations and maintenance history. Configure ingestion, OCR, storage and inference within the required boundary; check any external processing service before enabling it.
Explore deployment optionsQuestions & answers
What makes this different from basic OCR?
OCR reads text from an image. This workflow also maps that text to defined fields, validates values and returns structured rows with references to the source.
Can it extract from different document layouts?
It can be configured for varied layouts, but accuracy must be evaluated on representative documents. Poor scans and ambiguous fields should enter a review queue.
Will missing fields be filled automatically?
No. The supplied document must support the value. Missing or ambiguous fields remain clearly marked for review instead of being guessed.