Service / AI
From documents to data, without retyping.
Extraction from invoices, contracts, forms, IDs and scans into validated structured data, with confidence scores and review where it matters.
[ 01 ]The short answer
What is Document AI and Intelligent Document Processing?
Intelligent document processing (IDP) reads documents such as invoices, contracts, forms, statements and IDs, and turns them into structured, validated data your systems can use. Modern IDP combines OCR, layout understanding and language models, so it copes with formats it has not seen before. We add schema validation, cross-checks against your records and confidence thresholds, so clean documents flow straight through and doubtful fields go to a reviewer.
What you get
- Extraction pipeline per document type
- Validation rules and confidence thresholds
- Reviewer interface
- Integration to your systems
- Accuracy report per field
[ 02 ]What we build
The pieces, built to last.
- 01
Multimodal extraction
Text, tables, checkboxes, stamps and handwriting read from PDFs, scans and photos.
- 02
Schema validation
Every extraction validated against your schema and business rules before it is accepted.
- 03
Tabular review
Dozens of fields extracted across hundreds of documents at once, exported to spreadsheets.
- 04
Classification and splitting
Mixed bundles sorted into document types and split into the right records.
- 05
Reviewer workbench
Low-confidence fields highlighted on the page image for quick human correction.
- 06
Tamper and fraud checks
Signals for edited images, mismatched totals and reused documents.
[ 03 ]Where it pays off
Where it earns its keep.
- 01
Accounts payable
Invoices and receipts extracted and matched, at any layout.
- 02
Lending and KYC
Statements, payslips and IDs read and cross-checked for onboarding.
- 03
Legal review
Governing law, change-of-control and indemnity terms pulled from a data room.
- 04
Logistics and trade
Bills of lading, customs and shipping documents digitised for tracking.
[ 04 ]How we deliver
Four steps, one team, start to finish.
The same people scope it, build it and stay after launch. Timings are typical for a first release; we agree yours after the deep dive.
- 01Week 1
Find the decision
We pick the decision or task worth automating, measure how it is done today and agree what better means in numbers.
- 02Week 1 to 2
Build the eval first
A labelled test set from your own data comes before any prompt or training run, so every change is scored, not argued.
- 03Weeks 2 to 10
Build with guardrails
Models suggest, code decides. Permissions, injection checks, human override and audit logs are part of the first version.
- 04Ongoing
Ship and improve
We launch to real users, watch cost, latency and quality per request, and feed failures back into the eval set.
[ 05 ]Stack
Tools we reach for.
Chosen per project, and we work in yours. Nothing here is a lock-in: you own the code and the accounts.
- Gemini
- Claude
- GPT
- Azure Document Intelligence
- AWS Textract
- Docling
- Unstructured
- Tesseract
- Python
- Postgres
[ 07 ]Further reading
Problems we found, and how we would solve them.
- Retrieval3 min read
RAG still hallucinates. Grounding takes more than retrieval
ProblemRetrieval-augmented systems still invent facts, cite passages that do not support the claim, miss the decisive document and agree with false premises.ProposalTreat grounding as a pipeline with checks: structure-aware chunking, hybrid retrieval and reranking, citations at passage level that are verified after generation, abstention when evidence is missing, and evaluation of retrieval and answers separately.Read the proposal - Media integrity3 min read
Proving a photo is real when anyone can edit one
ProblemGenerative editing makes convincing image manipulation cheap, and morphed ID photos can let two people pass as one, while detectors alone lose ground with every new model.ProposalCombine provenance and detection: fingerprint originals at capture or registration, anchor the fingerprint and its owner in a tamper-evident registry, attach Content Credentials, and run region-level tamper detection on anything without provenance.Read the proposal
[ 08 ]FAQ
Questions buyers ask.
It depends on the document and the field, so we measure accuracy per field on your samples before launch, and set thresholds so uncertain values are reviewed rather than guessed.
No. Layout-aware models generalise across formats. We still test on the variety you actually receive.
Yes. Processing can run in your cloud account or on-premises with open models.
[ 09 ]More AI services
- Custom AI Model DevelopmentA model trained on your data, owned by you.
- LLM Fine-Tuning and DistillationFrontier quality at a fraction of the cost.
- AI Agent DevelopmentAgents that finish the job, inside the rules.
- AI Workflow AutomationHours of back-office work, done in minutes.
- RAG DevelopmentAnswers from your documents, with citations.
- AI Memory and Knowledge GraphsAI that remembers what your organisation learned.
- MCP Server DevelopmentYour product, usable by every AI assistant.
- Voice AI AgentsEvery call answered, qualified and logged.
- Private LLM DeploymentAI on your infrastructure, data never leaves.
- LLMOps, Evaluation and GuardrailsKnow your AI works before customers find out.
- Predictive ML and ForecastingDecisions made on what will happen next.
One company at a time