AI · Automation
Document intelligence that clears the backlog
An AI pipeline that reads, classifies and extracts structured data from messy documents, with a human in the loop where it counts.
In short
AI build, in short
- A parsing pipeline for mixed, messy document formats
- Confidence scoring with routing to human review
- Source-linked outputs that a reviewer can verify quickly
- An evaluation harness measuring accuracy on real data
What made this hard.
Document-heavy operations drown in manual data entry. Invoices, forms and contracts arrive in every format, and the cost is paid in hours and transcription errors.
The requirement was accuracy and traceability, not a black box. Wrong answers had to be catchable, and confident answers had to be trustworthy.
Stack
- Python
- Claude
- FastAPI
- PostgreSQL
- Next.js
- AWS
The build, step by step.
Extract, then verify
Documents are parsed into structured fields, and low-confidence results are routed to a person rather than guessed at.
Grounded outputs
Every extracted value links back to where it was found in the source, so a reviewer can check it in seconds.
Evaluation before trust
We build a labelled set and measure accuracy on real documents, so the system earns autonomy where it has proven itself.
Human in the loop
The pipeline handles the routine majority automatically and escalates the genuinely ambiguous cases to people.
What you walk away with.
- A parsing pipeline for mixed, messy document formats
- Confidence scoring with routing to human review
- Source-linked outputs that a reviewer can verify quickly
- An evaluation harness measuring accuracy on real data
Services involved
Document intelligence that clears the backlog questions, answered.
Still unsure if Document intelligence that clears the backlog is right for your project? A senior engineer will tell you straight on a free call.
Every output links back to its source in the document, and low-confidence results are routed to a person. Accuracy is measured on a labelled set of real documents before the system is given autonomy.
No. The pipeline handles the routine majority automatically and escalates ambiguous cases to a human. That keeps quality high while still clearing the backlog.
We default to current, capable models such as Claude and design the pipeline so the model is a replaceable component, not the whole system.
Building something similar?
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