Dense technical documents — inspection forms, census sheets, engineering reports — are still transcribed by hand into spreadsheets. This platform runs GPU-accelerated OCR on a remote machine over SSH, then extracts structured data from the result with LLMs, validated against a domain schema so the output is a dataset rather than a wall of text.
OCR alone does not solve document data entry. It converts pixels into text and leaves the hard part untouched: deciding which number is the span length, which date is the inspection date, and what to do when a field is missing or ambiguous.
Getting a language model to fill a schema is easy to demo and hard to trust — the same document can produce different answers on different runs, and without measurement nobody knows how often.
The benchmarking suite is the part I would keep if I could keep only one. It converts “the extraction looks good” into a number, and it makes regression visible when a model, prompt or chunking strategy changes. Without it, a document extraction system is a demo; with it, it is something you can put in front of a client and defend.
Shipped an automated extraction and evaluation engine with multi-run accuracy and consistency scoring against ground truth.
Estimate, not a logged metric: manual transcription of roughly 20–30 minutes per report reduced to under two minutes of automated processing.
Note: this case study is deliberately written without client names, product names or customer data. The time saving is an engineering estimate; accuracy and consistency are measured by the platform's own evaluation suite against ground truth.