AI extractor

Overview

An extractor converts unstructured text/media into defined fields. Unknown fields should remain unknown instead of being guessed.

Why this matters

Staff otherwise retype details from narratives and documents.

An everyday example

A clerk transfers invoice details into a form.

A technical example

Extract {date, vendor, total, evidenceLocations} into a validated schema.

How it works

1. Parse source
2. Schema-guided extraction
3. Type/domain checks
4. Review uncertain fields.

Architecture and flow

Text/PDF → model extraction → Zod/Pydantic → business validation.

The insurance assistant example

Populate a claim draft from an accident description.

When to use it

Variable formats that rules cannot cover economically.

When to avoid it

Use a structured API or stable parser when it is more reliable.

Interview answer

“Extraction produces proposed structured fields; validation and review decide what enters the business system.”

Benefits, trade-offs, and production considerations

Measure field-level accuracy, missingness, evidence support, and harmful invented values, not only JSON validity.