Audit and framing
Sample the documents, measure extraction difficulty and define the fields and acceptance criteria before building.
Document AI and OCR pipeline development
Documents become useful when their content reaches your business process as structured, validated data - not as a PDF attachment.
I build OCR and Document AI pipelines on AWS Textract, with tables, forms, layout, LLM extraction where it helps, and full traceability.
The situations this answers
- Manual re-keying of invoices, reports or technical forms.
- OCR that reads text but loses tables, structure and provenance.
- No validation step between extraction and the business process.
- No way to audit which document, page and model produced a value.
How it fits together
From documents to a consolidated report
A user uploads documents; S3 and Step Functions orchestrate extraction, seven parallel Pydantic AI agents cross-check the fields, then validation and a consolidated report close the run.
What you get
- A pipeline from document ingestion to validated structured data.
- Table, form and layout extraction with page-level provenance.
- A validation layer that flags uncertain values before use.
- Multi-model processing with sequential fallback.
- Traceability on every page and a documented operating procedure.
How an engagement runs
Design and delivery
Build ingestion, OCR, extraction and validation, then evaluate on a representative case set.
Industrialisation and takeover
Add monitoring, retries, cost controls and provenance, then hand over the runbook.
Proof and related reading
Frequently asked questions
Which documents can a Document AI pipeline handle?
Scanned PDFs, born-digital PDFs, forms, invoices, technical reports and multi-page documents with tables. Each corpus needs its own evaluation set.
Do you use OCR only or also language models?
Both, when it helps. Textract handles tables, forms and layout; language models add structure and interpretation, while validation keeps the output under control.
How do you handle errors and uncertain values?
Each field carries its provenance and a confidence signal. Uncertain values are flagged for review instead of being silently accepted.
Is the pipeline usable in a regulated environment?
The pipeline is designed for traceability: every page, model and result can be audited. Compliance requirements are collected during framing and validated with your team.
Can the pipeline run inside our AWS account?
Yes. It is built with your account, your IAM policies and your data residency constraints, and it can reuse your existing storage and queues.