Case Study
Replaced manual PDF review with an OCR + extraction + validation pipeline. Engagement shipped in 18 days with a human-in-the-loop review queue and structured outputs feeding downstream systems.
Outcome
50K+
Documents processed
first 30 days
82%
Manual work removed
vs. baseline
18d
From kickoff to production
<1%
Error rate
human-reviewed
Architecture
The Problem
The team was processing thousands of supplier and compliance PDFs per week by hand. Three full-time analysts split their day between data entry, exception triage, and reporting.
Earlier OCR pilots failed because the output had no validation layer and dumped raw text into spreadsheets that no downstream system could trust.
Our Approach
We mapped the document taxonomy in week one, locked the canonical schema, and identified the three highest-volume document classes that drove most of the manual labor.
The pipeline ingests via S3 drop, runs OCR through a vendor model, applies field-level extraction prompts, and pushes results into a Postgres schema with strict types.
Every record gets a confidence score. Anything below the threshold lands in a review queue with the original document side-by-side; reviewers approve or correct in a single click.
The Outcome
The pipeline now ingests 50K+ documents in the first month with a sub-1% post-review error rate.
Two of the three analysts moved off data entry into exception handling and process improvement.
Downstream reporting pipelines pull directly from the structured schema, so the operations leader gets a live view that used to lag by a week.
Tech Stack
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