Every business has data trapped in the wrong shape: PDFs, scanned forms, emails, chat exports, spreadsheets with inconsistent columns. AI data processing uses models to extract, normalise, classify and validate that information so it can drive reports, systems and decisions.
The pipeline
Ingest → pre-process (OCR, splitting, cleaning) → extract with a model into a defined schema → validate with rules and cross-checks → route confident records automatically and uncertain ones to review → load into the destination system → monitor accuracy.
Design principles
Define the output schema first. Use structured output modes so models return typed JSON. Validate everything the model produces against business rules (totals add up, dates are plausible, IDs exist). Keep the original document linked to every extracted record. Measure field-level accuracy on a fixed sample.
Common sources
Invoices and receipts, KYC and onboarding documents, contracts and agreements, survey and feedback text, support conversations, product catalogues from suppliers and regulatory filings.
My perspective
Data processing is where AI produces the least glamorous and most reliable returns. It also builds the clean datasets that make every later AI initiative possible.
Business use cases
- Invoice and receipt extraction into accounting systems
- KYC and onboarding document verification and data capture
- Contract clause extraction and comparison
- Feedback and review categorisation with sentiment and themes
- Supplier catalogue normalisation
- Historical archive digitisation and indexing
Limitations to be honest about
- OCR quality limits extraction quality on scanned documents.
- Rare document layouts may need explicit handling.
- Validation rules require domain knowledge to write well.
- Accuracy targets must be realistic; 100% without review is not a plan.
How I approach it
- We agree the target schema and accuracy threshold, assemble a labelled sample, build the extraction and validation pipeline, and run it in shadow mode against manual processing before switching over with a review queue for exceptions.
Frequently asked questions
How accurate is AI document extraction?
On clean, digital documents field-level accuracy is typically high; scanned or handwritten inputs reduce it. Validation rules and a review queue close the gap.
Do we need to train a custom model for our documents?
Usually not at first. Foundation models with structured outputs handle many layouts. Fine-tuning is a later optimisation if measured accuracy demands it.
Where does the extracted data go?
Into your existing systems — accounting, CRM, databases — with the source document linked for audit.
Last updated 11 September 2026