Turn documents into useful, structured information without copying every field by hand. I build bespoke AI document processing tools for invoices, purchase orders, PDFs and emails, with validation, exception handling and review before data reaches your business systems.
AI Document Processing
Document extraction needs to account for changing layouts, poor scans and missing information. I combine suitable text extraction or OCR with AI interpretation and explicit validation rules.
Capture fields such as supplier details, references, dates and line items. Check required values and calculated totals with code before preparing a record for review.
Choose text extraction for digital PDFs and OCR where scans require it. Account for rotation, low image quality and multi-page inputs rather than treating every document as clean text.
Collect approved document types from an upload flow or supported mailbox integration. Validate file types and sizes, control access and track each document through processing.
Show extracted fields alongside the source so staff can correct uncertain results. Export approved data through supported APIs or structured files, with duplicate checks and visible delivery status.
Delivery and controls
A demonstration on one tidy invoice is not enough. Scope and evaluation should reflect the layouts, languages and document quality your team encounters in practice.
Agree a data structure, required values and field-level validation. Identify which details can be missing and which should prevent a document moving to the next stage.
Include varied suppliers, multi-page documents, unfamiliar layouts and poor scans. Measure field accuracy and correction effort rather than assuming a model’s self-reported confidence is reliable.
Connect each proposed record to its source document. Give reviewers a clear way to approve, correct or reject extraction results and record the final decision.
Agree retention and access rules for original files and extracted data. Protect exports, detect duplicate submissions and keep failures recoverable when a destination system is unavailable.
Example project
This illustrative workflow prepares structured invoice data for checking. It does not authorise payments or assume every extracted field is correct.
Validate the uploaded file, read its text or run OCR, then extract the agreed invoice fields into a structured draft.
Check dates, references, totals and duplicates. Show exceptions and the source document to an accounts reviewer.
Pass reviewed fields to the accounting workflow through an agreed integration. Track the outcome and retain a clear audit trail.
Tell me what happens today, which systems are involved and what a useful improvement would look like.
AI Document Processing FAQ
Often, by combining OCR with structured extraction. Results depend on scan quality, handwriting, language and layout. Representative samples are needed to assess whether the approach is suitable.
There is no honest universal accuracy figure. I evaluate the fields you need against representative documents and include validation and review where errors matter. Accuracy and review effort should be measured together.
Variable layouts are a useful reason to consider AI extraction. Unfamiliar or incomplete documents still need checks and an exception path; support should be evaluated against the types you actually receive.
Approved data can be sent through a supported API or agreed export format. Whether any fields can bypass manual review depends on validation results, the impact of errors and your process requirements.
Storage location, permissions and retention are agreed during scoping. The implementation should send only necessary information to any external provider and provide an appropriate deletion process.
