Document ingestion and field extraction

Ingestion should preserve source channel, document type, and key metadata before extraction begins. Teams create avoidable rework when everything enters the queue as a generic file and only later gets sorted through manual interpretation.

The Upload tab is the controlled entry point for that discipline. Operators choose the OCR provider, optionally supply a document-type hint, upload the image or PDF, and then run OCR so the system can create the ingested and extracted document records with a known intake path.

Evidence should come from document intake, provider hint, extraction confidence, review queue state, import audit, alias handling, or downstream workflow result. For Capture with structure, a strong answer names the visible cue, record, status, or reference that supports the next step and states what would pause the learner.

Fields are only valuable when they map to the workflow that will validate, post, or route the document. OCR quality is not just about reading text correctly; it is about delivering the right structured signal to the next operational decision.

After extraction, the runtime exposes normalized document type, overall confidence, and whether review is required. That matters because upload is not finished when text is recognized; it is finished only when the result is reliable enough for review or import routing.

For Extraction in workflow context, the learner should point to the specific page, record, status, or note that separates evidence from assumption before moving to the next step.

Run the lesson as an automation quality review. Start with the practical task: identify the major stages of document intake. Ask the learner to name the role, surface, evidence, and state they would inspect before taking action.

Evidence should come from document intake, provider hint, extraction confidence, review queue state, import audit, alias handling, or downstream workflow result. The practice should end with the learner connecting the action back to the lesson summary: teach automation teams how good document intake separates capture, classification, and extraction so downstream workflows can trust the result.

Close the exercise by asking the learner to restate the objective in operational terms: identify the major stages of document intake. They should name what changed, what remains uncertain, and which surface or owner takes the next step.

Do not let automation quality be judged by volume alone. Confidence, review routing, import evidence, and exception resolution matter more than raw document count. In this lesson, watch for that risk while learners work on this objective: identify the major stages of document intake.

Do not mark the lesson complete because the learner can repeat terms. Completion means they can explain when to set a document-type hint or change OCR provider before running extraction and describe why the lesson matters in real work.

Review the answer for skipped ownership, missing evidence, or vague next steps. If the learner cannot explain when to set a document-type hint or change OCR provider before running extraction, keep the lesson in practice mode before marking it complete.