Document Intelligence (BFSI)
Reads, classifies, extracts and validates documents end to end, with a human only where it matters.
The brief
Teams were keying critical financial data by hand from invoices, balance sheets, financial statements and other paperwork arriving in many formats. The work was slow, error-prone and could not scale with growing document volume, while still having to meet strict regulatory and data-privacy requirements.
The client needed a pipeline that could read almost anything, extract the fields that mattered, validate them against business rules, and escalate only the cases a person genuinely needed to see.
- Stop keying data by hand from high volumes of mixed-quality documents.
- Read scans, photos and forms across several layouts, including handwriting.
- Validate every extracted field against business and compliance rules automatically.
- Escalate only genuinely uncertain documents to a human reviewer.
- Maintain a verifiable, field-level audit trail for compliance.
- Scale with growing document volume while meeting regulatory requirements.
Our AI-native approach
We built an IDP/ICR pipeline that treats each document as a sequence of decisions (classify, extract, validate, route), each with a confidence score. High-confidence documents pass straight through; anything uncertain is sent to a focused human-in-the-loop review with the model's best guess pre-filled.
OCR handles both structured and unstructured formats, a custom transformer-based validation model checks extracted data against regulatory accuracy standards, and a generative AI layer adapts extraction across varied document types with less manual oversight.
What we built
Multi-format ingestion
Scans, photos and digital files are normalised, de-skewed and quality-checked on entry.
Classification
Each document is routed to the right extraction model by type and layout.
OCR & ICR extraction
Printed and handwritten content is read, with field-level confidence on every value.
Rule-based validation
Extracted fields are checked against business and compliance rules automatically.
Human-in-the-loop review
Only low-confidence cases reach a reviewer, pre-filled with the model's proposal.
Audit trail
Every field carries its source, confidence and reviewer action for full traceability.
How we built it
Discovery began with the document corpus itself: we mined the real distribution of layouts, languages and edge cases so the pipeline was designed for reality rather than an idealised sample. We made confidence a first-class concept early on, because it is precisely what lets the system route work safely between automation and human review.
We delivered by document type in phases, each gated on accuracy against a human-labelled set before going live. Reviewer corrections were wired back into training from day one so accuracy compounded rather than plateaued, while integrating cleanly with the client's existing systems.
How it works
- Step 01
Ingest
Documents are normalised and quality-scored.
- Step 02
Classify
The type and layout are identified.
- Step 03
Extract
OCR/NLP pulls structured fields with confidence.
- Step 04
Validate
Rules and cross-checks confirm or flag each value.
- Step 05
Route
Clean documents pass through; uncertain ones go to review.
The extraction stack pairs OCR with NLP classification and named-entity models tuned to the client's document set. Confidence is first-class: it decides routing, so the system gets faster as the models learn, and reviewers spend their time only on genuine ambiguity.
Validated corrections feed back into training, so accuracy compounds rather than plateaus.
The impact
Up to 80% less manual work across document handling.
35% improvement in extraction accuracy and 45% reduction in processing time.
Full regulatory compliance maintained throughout.
Reviewers shifted from data entry to genuine exception handling.
Technology stack
Related service: Document AI & Intelligent Document Processing.
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