Turn product documents into structured data.
A human-in-the-loop extraction workflow that turns messy product pages, labels, and documents into reviewable structured records.
- 01Product source
- 02AI extraction
- 03Human review
- 04Structured record
Use AI to accelerate extraction while keeping validation and judgment in the loop.
The prototype explores a common operating problem: useful product information exists, but it is trapped in inconsistent pages and documents. The workflow extracts candidate fields, preserves source context, and routes uncertain values for review before they become trusted data.
Manual product research is slow and inconsistent. Important fields may appear under different names, live in images or PDFs, or be missing entirely. Copying them directly into a database creates silent quality problems.
The system collects source content, extracts candidate values into a defined schema, attaches confidence and source context, flags missing or conflicting fields, and presents the record for human review.
AI accelerates the first pass; it does not get final authority. A reviewer can inspect the source, correct values, reject weak evidence, and approve only the fields that are ready to use.
Schema design, AI-assisted extraction, source traceability, exception handling, and a practical way to combine automation with human judgment.
This is a working prototype and portfolio demonstration, not a public production service.
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