Intelligent Document Processing and OCR
Extraction that turns invoices, forms, contracts and statements into validated structured data at volume, with per-field confidence and a review queue that only receives the cases that genuinely need a person.
Document processing is judged on one number that most projects never define: straight-through rate, the share of documents that reach a correct outcome with nobody looking at them. A system with excellent average accuracy and no confidence signal produces a review queue containing every document, which is the process you already had.
Intelligent document processing is the automated conversion of documents into validated structured data: classifying the document type, extracting fields with layout and context understood, scoring confidence per field, validating against your own systems and business rules, and routing only low-confidence or failing cases to human review.
Straight-through rate is the measure that matters
Accuracy per field is necessary and not sufficient. What determines the business case is how many documents complete without a person, and that depends on three things working together:
- Extraction accuracy on the fields that matter, weighted by consequence rather than averaged across all fields.
- Calibrated confidence, so that a high-confidence field is genuinely reliable and a low-confidence one is genuinely doubtful. Uncalibrated confidence is worse than none, because it routes the wrong cases.
- Validation rules, checking extracted values against your systems: does this supplier exist, does the total equal the sum of lines, is this policy number active.
Together these determine what can be trusted unattended. We report straight-through rate at your accuracy requirement, and the trade between them, rather than a single accuracy figure.
How we build extraction that scales
Classify before extracting
A mixed intake of invoices, credit notes, delivery notes and correspondence needs routing first. Misclassification is a silent failure mode: extracting invoice fields from a credit note produces plausible values that are wrong in the direction that matters most.
Preserve layout and structure
Tables, line items, headers, stamps, handwriting and multi-page documents. A table flattened into prose loses the row and column relationships permanently, and no downstream processing recovers them. Every extracted value keeps a pointer to its coordinates on the page so any figure can be checked against the original in one click.
Validate against reality
Cross-field arithmetic, master data lookups, date and format rules, and duplicate detection. Validation catches a large share of extraction errors before they reach a person, and it is usually cheaper to improve than the model is.
Calibrate confidence honestly
We measure whether the confidence score predicts correctness on held-out documents and tune until it does. This is what allows a threshold to be set responsibly, and it is the step most often skipped in favour of a vendor's default.
Design the review queue as a product
The reviewer sees the document, the extracted values, the flagged fields and the reason, with the source location highlighted. Corrections are captured as training data. A review interface that requires re-typing everything wastes most of the benefit of the extraction.
Handle the long tail explicitly
Every intake has unusual formats, poor scans, foreign languages and documents that should not have been sent. These are routed rather than forced through, and their volume is reported so the exception path stays visible instead of quietly absorbing effort.
Where this differs from multimodal AI
Multimodal AI development reasons across mixed media to answer open questions. Intelligent document processing is a production pipeline for high-volume structured extraction with confidence, validation and straight-through processing. Many builds use vision-language models inside the pipeline; the difference is the operational apparatus around them.
What it replaces
| Document | Fields typically extracted | Validation that catches errors |
|---|---|---|
| Supplier invoices | Supplier, dates, lines, tax, total | Purchase order match, arithmetic, duplicate check |
| Delivery and goods notes | Reference, quantities, condition | Match against order and invoice |
| Application forms | Applicant details, declarations | Master data lookup, format rules |
| Bank and financial statements | Transactions, balances, periods | Balance continuity, date sequence |
| Contracts | Parties, dates, values, key clauses | Term consistency, signature presence |
| Identity documents | Name, number, dates, issuer | Expiry, checksum, format by country |
Where extraction is one step inside a larger process with exceptions to resolve, it pairs directly with agentic workflow automation.
How the engagement runs
Your real documents, including the bad ones, decide what is achievable before anything is built.
Document audit and baseline
A representative sample including poor scans and edge cases assessed, current handling time and error rate captured.
Classification and extraction
Document routing, layout-aware extraction, coordinate traceability, measured per field on the sample.
Validation and confidence
Business rules and system lookups implemented, confidence calibrated against held-out documents, thresholds set.
Review queue
Exception interface with highlighting and correction capture, integrated into your operational system.
Pilot and handover
Parallel running against current process, straight-through rate measured, then handover with retraining guidance.
What you receive
A pipeline measured on straight-through rate, with a review queue that only sees what it should.
Extraction pipeline
Classification, extraction and validation deployed as infrastructure as code.
Field-level accuracy report
Per field and per document type, on your own sample including the difficult cases.
Confidence calibration
Evidence that confidence predicts correctness, with the thresholds derived from it.
Validation rule set
Cross-field, master data and format rules, documented and maintainable by your team.
Review interface
Exception queue with source highlighting and correction capture feeding retraining.
Straight-through reporting
Rate by document type against the pre-automation baseline, with the exception mix.
Is this the right engagement?
Worth being direct. Intelligent Document Processing and OCR is the wrong spend in some situations, and those are listed rather than buried.
Good fit if
- People currently key data from documents into systems at volume.
- Document types are repetitive even if formats vary between senders.
- Extracted values can be validated against systems you control.
- A review queue is acceptable for the cases that need it.
- You can supply a representative sample including the poor-quality documents.
Choose something else if
- Volume is low enough that keying is cheaper than building.
- Every document is unique with no repeating structure.
- The requirement is open-ended reasoning across documents rather than extraction. See multimodal AI development.
- Accuracy must be absolute with no review path, on documents that are genuinely ambiguous.
Frequently asked questions
Marked up with FAQPage schema so these answers can surface directly in search results and inside AI assistant responses.
What accuracy can intelligent document processing achieve?
On clean digital documents with consistent layouts, high enough that the exceptions become the work. On poor scans, handwriting and unusual formats it varies enough that a review path is mandatory. We establish which you have by running your real sample in the first fortnight rather than quoting a benchmark figure.
What is straight-through processing rate?
The share of documents that reach a correct outcome with no human involvement. It is the number the business case rests on, and it depends on extraction accuracy, calibrated confidence and validation rules together. A system without calibrated confidence has to route everything to review, which is the process you already had.
How is this different from traditional OCR?
OCR converts pixels to characters. Intelligent document processing understands that a value sits in a particular table cell under a particular heading, validates it against your systems, scores its own confidence and decides whether a person needs to look. OCR is often still a component inside it.
Can it handle documents in several languages?
Yes, with accuracy measured per language rather than averaged, because it varies. Scripts other than Latin and mixed-language documents need explicit testing, and we report per-language results before any language goes live unattended.
What happens to documents it cannot process?
They are routed to the exception queue with the reason attached rather than forced through with low-confidence values. The volume and mix of exceptions is reported, so the long tail stays visible instead of quietly absorbing your team's time.
Often paired with this
Most clients combine two or three engagements from the Computer Vision pillar. These are the ones that most often run immediately before or after.
Object Detection and Image Classification
Detection and classification models trained on your own images, with honest labelling and cost-weighted thresholds.
Read more →Medical Imaging AI
Imaging AI built around the regulatory path and clinical validation, not around a benchmark score.
Read more →Image Generation and Enhancement Pipelines
Production pipelines for generated and enhanced imagery, with rights, provenance and automated quality gates.
Read more →Is this the right engagement?
Tell us what you are trying to build. If a different service fits better, or if you do not need us at all, we will say so.