Replace document-triage teams with hybrid OCR + LLM pipelines. Invoices, medical records, contracts, claims — extract, classify, route, and verify automatically.
Every business runs on PDFs, scans, and forms — and most of them are still being keyed in by hand. We build document pipelines that read invoices, parse contracts, extract medical records, and verify insurance claims with 95%+ field-level accuracy on real-world documents (not the cherry-picked demos).
We've learned the hard way that pure OCR plateaus at ~80% accuracy on messy real-world inputs and pure LLM extraction is too expensive to run at enterprise scale. Our hybrid architecture combines OCR for layout understanding with LLM-based reasoning for semantic extraction — backed by an active-learning human-in-the-loop layer so the system gets better every month instead of drifting.
The four things you'll get from a Niche.dev engagement on document ai & automation.
Layout-aware OCR for structure, LLM for ambiguous fields, human review only on low-confidence edges. Average production accuracy: 95–98% per field on real documents.
Every healthcare pipeline runs on BAA-covered model providers with on-the-fly PHI redaction, encrypted at-rest storage, and full audit trails ready for compliance review.
We tell you the per-page cost upfront and design for it. Most pipelines run $0.02–$0.10 per page including human review on the long tail.
Every human correction feeds back into the model's prompt or fine-tune set. Accuracy goes up month-over-month instead of drifting down.
Concrete systems we've shipped in this space. Not a roadmap — production deployments.
Predictable, milestone-based, no open-ended retainers. You see real progress every two weeks.
Send us 100–200 representative documents. We benchmark accuracy of off-the-shelf tools vs. a proposed hybrid pipeline and quantify the long tail before we build anything.
Three-week build of an end-to-end pipeline for one document type (e.g., invoices). You see real per-page costs and accuracy numbers on your real documents, not benchmark sets.
We wire it into your ERP / EHR / DMS, build the human review UI, and start with a low-stakes document type. Phased rollout with confidence-threshold gates.
Monthly accuracy reviews, prompt and fine-tune updates, and progressive coverage of new document types. Most clients hit 5+ document types in the first year.
We bring opinions but we meet you where you are. These are the tools we use most for document ai & automation.
Every one of these is a production system. Click through for the full case study.
The questions every prospect asks before working with us.
Send us 100 representative documents. We'll benchmark current accuracy vs. a proposed hybrid pipeline and send back a real cost-and-accuracy projection — no slideware.
Email Nick directly