Documents in. Validated data out.
We capture, classify, extract and validate data from invoices, statements, KYC packs, claims and contracts, and send only the uncertain fields to people.
Data arrives trapped in documents.
Most operational data still enters the organisation as a PDF, a scan or an email attachment. Teams read it, key it into an ERP or core system, and check each other's work. It is slow, error-prone and hard to scale at month end.
Template OCR breaks the moment a supplier changes its layout. Modern IDP combines layout-aware extraction models, classifiers and business validation rules. Each field carries a confidence score. High-confidence documents flow straight through. Low-confidence fields go to a validation station, and every correction feeds back into the model.
Models read fields by meaning and position together, so new supplier formats do not need new templates.
Thresholds are set per field, so a low-risk reference field never holds up a payment.
Totals, tax maths, master data lookups and PO matching catch errors a model cannot see.
Human corrections are captured as labelled data for scheduled retraining.
End-to-end document pipelines.
Ingestion
Intake from shared mailboxes, SFTP drops, scanners, portals and APIs, with de-duplication and page splitting of multi-document files.
01Classification
Separation and typing of mixed packs, such as a KYC bundle holding ID proof, address proof, bank statement and signed forms.
02Extraction
Header fields, line items, tables and handwriting from invoices, POs, bank statements, claims forms and contracts.
03Validation rules
Arithmetic checks, GSTIN and VAT format checks, vendor and PO lookups, date logic and cross-document consistency.
04Human-in-the-loop stations
Validation queues where reviewers see the source image beside each flagged field and correct it in seconds.
05Feedback and retraining
Corrected fields stored as training data, with model versions evaluated against a held-out set before release.
06From sample set to straight-through.
Profile the documents
We collect a representative sample by type, source and quality, and measure layout variety, page counts, languages and scan quality.
Define the schema
With the business we fix every field, its format, its downstream system and the risk of getting it wrong. That risk sets its threshold.
Build and train
We configure pre-trained models first, fine-tune where accuracy falls short, and label a ground-truth set for honest measurement.
Tune thresholds
Field-level precision and recall are measured against ground truth. Thresholds are set so that what passes straight through is reliably correct.
Operate and improve
Validation station corrections are reviewed, models are retrained on a schedule, and STP rates are tracked by document type and source.
Extraction engines and the glue around them.
IDP platforms
Cloud AI services
Human validation
Data and integration
What a tuned pipeline typically delivers.
Document turnaround
Invoices and forms are posted the same day they arrive, including at quarter end.
Straight-through rate
Clean, known document types often pass with no human touch at all.
Review effort
Reviewers confirm flagged fields instead of keying entire documents.
Traceability
Every posted value links back to its source page, confidence score and reviewer.
Do you need thousands of samples to start?
No. Pre-trained models for invoices, receipts, IDs and bank statements work with little or no training. Custom documents such as contracts or claims forms usually need a few hundred labelled samples per type to reach production accuracy.
How do you set confidence thresholds?
Per field, based on the cost of an error. Bank account numbers and amounts get strict thresholds with mandatory validation. Low-risk descriptive fields can pass at lower confidence.
Can it handle handwriting and poor scans?
Within limits. Modern models read clear handwriting and moderate scan quality well. We measure accuracy on your worst samples during profiling and route anything below threshold to review.
Where do LLMs fit in document processing?
They are useful for long, variable documents such as contracts and correspondence, for summarisation and for classifying ambiguous items. We pair them with schema validation and source citation so outputs are checkable.
Where is the data processed and stored?
In your chosen cloud region or on-premise, under your keys where the platform supports it. We design retention, masking of PII and access controls to match your data protection obligations.
Find your first ten automations in two weeks.
A structured discovery sprint that maps your processes, scores them for automation potential and hands you a prioritised roadmap with effort and payback for each.