What we build
From raw input to usable knowledge.
The full path a document travels — read, extracted, made searchable, and checked. Each stage is in production inside ScribeAI today.
OCR & document reading
Read handwriting, scans, PDFs, and forms — the inputs most tools choke on. The same handwriting OCR that powers ScribeAI answer evaluation.
- Handwriting & printed text
- Multi-page papers & forms
- Layout & table awareness
Extraction & structuring
Pull the fields, tables, and entities that matter into a clean, consistent schema — so raw documents become data your systems can use.
- Field & entity extraction
- Tables → structured rows
- Your schema, enforced
Retrieval & RAG
Make your knowledge searchable and ready to ground AI answers — the retrieval layer that keeps ScribeAI's scoring tied to real syllabus context.
- Semantic search over your corpus
- Grounded, cited answers
- Vector + rerank pipeline
Validation & review
Low-confidence extractions get flagged for a person instead of passing silently — so the structured data you rely on is data you can trust.
- Confidence scoring
- Human check on edge cases
- Correction feedback loop
Why you can trust it
Data you can actually rely on.
Extraction is only useful if you can trust what comes out. Four things we build into every data pipeline we ship.
Built for the real world
Smudged handwriting, bad scans, odd layouts — the messy inputs real businesses have, not clean sample data.
Grounded & searchable
Extracted data is indexed and retrievable, so AI answers stay tied to your actual sources — not a model's guess.
Validated, not assumed
Confidence is scored on every field; anything uncertain routes to a person before it becomes data you depend on.
Private & isolated
Your documents and data stay yours — isolated per customer, the same multi-tenant separation ScribeAI runs on.