Intelligent document processing services for BFSI, insurance and logistics

It replaces manual keying of bills of lading, freight invoices and shipping records. Document images are read with OCR and AI analysis, including handwritten fields, the values are validated against existing shipment and customer records, and clean data is posted into the logistics or transport system. Staff review only the fields the pipeline is unsure about, which cuts manual data entry and the errors that come with it.

We have built extraction for bills of lading, invoices and shipping records, including handwritten details, for a logistics and transportation operator. Proof-of-delivery slips, delivery receipts, packing lists, rate confirmations and customs papers follow the same pattern of classify, extract, validate and route. Send a small anonymised sample and we will tell you which fields can be read reliably before any build starts.

OCR converts an image of text into machine-readable characters. Intelligent document processing goes further: it classifies the document, extracts specific fields, validates them against rules and records, scores confidence and routes exceptions to a person. OCR is one component inside an IDP pipeline, alongside ICR for handwriting, NLP and, increasingly, LLM-based extraction.

A SaaS IDP product is usually the faster choice for common, printed documents like standard invoices. A custom pipeline pays off when you have handwriting, domain terminology such as medical notes, unusual layouts, validation against your own records, strict data-residency or privacy requirements, or volumes where per-page pricing gets expensive. It also lets you own the models and retrain them on your corrections.

Each extracted field gets a confidence score. You set a threshold per field, stricter for amounts and IDs, looser for descriptive text. Fields above it go straight through; anything below goes to a review screen with the document image and the model's best guess pre-filled. Reviewer corrections are logged for audit and used to retrain, so review volume falls over time.

Yes. For two insurers we fine-tuned a handwritten OCR model for doctor notes and prescriptions, paired with medical NLP, which cut manual review time by 40% and made claims processing 25% faster. For a logistics and transportation operator in India we built extraction that captures handwritten fields on bills of lading, invoices and shipping records.

Yes. For two Indian BFSI groups we automated extraction from invoices, balance sheets and financial statements using OCR, a transformer-based validation model and a generative AI layer, reporting a 35% accuracy improvement and 45% less processing time. The same pipeline pattern applies to KYC packs and loan files: classify, extract, validate against rules, and route exceptions.

We use LLMs where layouts vary too much for templates, but never trust their output alone. The model returns structured fields against a fixed schema, each field is validated against business rules and existing records, and confidence thresholds send doubtful values to a reviewer. OCR and dedicated models still handle the parts they do reliably and more cheaply.

Pipelines are designed around your privacy obligations. For insurance clients we built a secure processing pipeline to HIPAA and GDPR compliance standards, and our BFSI work was designed to meet strict regulatory and data-privacy requirements. We can deploy into your own cloud, restrict which models see which data, and keep a field-level audit trail.