Service
Document AI & Intelligent Data Extraction
Someone reading PDFs and typing them into your system?
VelvoAI turns unstructured paperwork into data your systems can act on. Invoices, contracts, claims forms, purchase orders, statements — read, checked against your rules, and pushed into the right system with a confidence score attached, so anything doubtful routes to a person instead of resolving quietly.
Built by VelvoAI, an independent AI systems studio. One engineer scopes and writes every system.
What the system does
- Per document
- Seconds
- Field confidence
- Scored
- Full trail
- Audited
- ComponentAzure Document AI
- ComponentOpenAI Vision
- ComponentPython
- ComponentPostgres
- ComponentQueue Workers
The problem
Why this keeps costing you
Document handling is quietly one of the most expensive processes in most businesses. Someone opens a PDF, reads it, retypes eight fields into another system, files it somewhere, and repeats. It is slow, it is error-prone, and the errors surface weeks later in a reconciliation nobody enjoys.
What we build
Inside a document AI pipeline we ship
- OCR and layout parsing for scans, photos, native PDFs, and email attachments
- Field extraction with per-field confidence scoring rather than all-or-nothing output
- Validation rules that check totals, dates, references, and cross-document consistency
- Human review queues for low-confidence extractions with single-screen correction
- Straight-through posting into ERP, accounting, or CRM systems
- Classification that routes each document type to the right pipeline automatically
How we approach it
The part most implementations skip
We design for the ninety percent and route the rest to people. Documents that extract cleanly and pass validation flow straight through. Anything below your confidence threshold, or failing a business rule, lands in a review queue where a person fixes it in seconds — and that correction feeds back into extraction quality.
Deliverables
What you actually receive
Every VelvoAI engagement ends with working software you own, not a slide deck. Here is what ships with a document AI pipeline.
How a build runs, step by step- D.01Extraction schema per document type agreed with your team
- D.02Accuracy benchmark measured on a sample of your real documents
- D.03Review queue interface for exceptions
- D.04Integration writing extracted data into your system of record
- D.05Retention and audit configuration matching your compliance requirements
By industry
Document AI in your sector
Document AI, applied to the workflows and rules of the sectors we work with most.
- 01
Document AI for Law Firms
First-pass contract review is expensive and profoundly repetitive.
- 02
Document AI for Financial Services
Statements arrive in forty formats and get keyed by hand.
- 03
Document AI for Healthcare
Referral letters arrive as PDFs and leave as retyped records.
- 04
Document AI for Logistics
Every consignment carries paperwork somebody has to read.
- 05
Document AI for Real Estate
Compliance paperwork is filed by hand and audited under pressure.
Common Questions
01What accuracy should we expect?
Clean native PDFs of a consistent format extract very reliably. Poor scans and highly variable layouts are harder. We benchmark on your actual documents before committing to a threshold, and the confidence scoring means low-certainty extractions get reviewed rather than silently posted.
02Do we still need someone reviewing documents?
For a much smaller share. The goal is that your team reviews the exceptions rather than every document — which typically means a fraction of the volume, handled in seconds each rather than minutes.
03Can it handle documents in multiple languages?
Yes. Multilingual extraction is well supported. Right-to-left scripts and handwritten fields warrant a test on your real samples first, which we do during scoping.
Related services
Often built alongside this
- Written by
- Usman Ali, Founder
- Last reviewed
Next step
Ready to scale operations without more busywork?
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