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Extraction
Fields, tables and line items pulled into a structured record.
DEV-03 · DOCcstmAI™ · Development · Document & vision AI
Document AI for the paperwork your business runs on.
We build document and vision AI that turns intake packets, invoices, forms, scans and drawings into structured records, with a confidence score on every field and a review queue for anything uncertain.
Fig. 01 · Wiring an experimental aircraft, NASA Langley
Document processing is where AI pays back fastest and fails most quietly. A model that is right 95% of the time is still wrong on one field in twenty, so the system around it matters as much as the model: confidence thresholds, validation against your records, and a quick review screen for the rest.
We combine OCR, layout and vision models with open-weight language models, tuned and evaluated on samples of your documents. It all runs on your hardware, which matters when the documents are medical, legal or financial.
| Inputs | Scans, PDFs, photos, email attachments, faxes, handwriting |
|---|---|
| Outputs | Structured fields into your systems through cstmAI Connect |
| Quality controls | Per-field confidence, validation rules, human review queue |
| Measured as | Field-level accuracy on a labeled sample of your documents |
| Starts with | A two-week discovery sprint with a labeled sample |
What we build
From a stack of paper to a record you can trust.
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Classification and splitting
A mixed packet separated into documents and labeled by type.
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Validation
Extracted values checked against your ERP, policy or master data.
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Drawings and images
Title blocks, notes and photos indexed and linked to records.
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Handwriting
Read where it can be, flagged for review where it can't.
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Review queue
A fast screen for people to confirm the uncertain fields.
Reel 02 · Lathe aisle seen from the catwalk, Paterson, NJ1994
How it's delivered
Four steps, each one signed off.
01
Sample & label
Collect representative documents, including the worst ones, and label them.
02
Baseline
Run current models and report field-level accuracy.
03
Tune
Improve extraction, validation and thresholds against the sample.
04
Deploy with review
Go live with a review queue; tighten thresholds as accuracy proves out.
FAQ
Questions we hear first.
What accuracy can we expect?
It depends on your documents, so we measure instead of promising. In discovery we label a sample, run a baseline and report field-level accuracy before you commit to a build.
Can it read handwriting and poor scans?
Often, with lower confidence, which is what the review queue is for. We test your worst examples early.
Where do the results go?
Into your system of record through cstmAI Connect, or to the review screen first when confidence falls below your threshold.
Get a quote
Spec your system.
Tell us the models you want to run, how many people will use them and where the hardware should live. An engineer replies with a first configuration and the questions that decide the quote.