Intelligent document processing services for BFSI, insurance and logistics
We build document AI pipelines that read scans, photos and PDFs, including handwriting, then classify, extract, validate and route the data into your core systems, with people reviewing only the uncertain cases.
MappOptimist builds custom intelligent document processing systems for banks, insurers and logistics operators. Our pipelines combine OCR, ICR handwriting recognition, NLP and LLM-based extraction with confidence scoring and human-in-the-loop review, then integrate results into your existing systems. An India-based engineering team delivers to clients worldwide as a fixed build, a managed pod or embedded AI engineers.
Problems we solve
Teams still key data by hand
Invoices, statements, KYC packs and shipping records arrive as scans and phone photos in dozens of layouts. Manual entry is slow, error-prone and cannot absorb peak volume without temporary staff.
OCR reads the text but not the meaning
Template-based OCR breaks when a layout changes and returns raw text, not validated fields. Someone still has to find the policy number, the consignee or the net amount and check it.
Handwriting defeats the off-the-shelf tool
Doctor notes, prescriptions and handwritten fields on bills of lading are where generic tools fail, and they are often the fields that decide a claim or a delivery.
Compliance needs a trail for every field
Regulated teams must show where each value came from, who approved it and which rule it passed, while keeping financial and health data inside strict privacy controls.
What we build
Multi-format ingestion and classification
Scans, photos and digital files are normalised, de-skewed and quality-scored on entry, then classified by document type so each one follows the right extraction path.
OCR, ICR and LLM-based extraction
OCR for printed text, fine-tuned ICR models for handwriting, and NLP or named-entity models for fields. A generative AI layer handles varied and unstructured layouts where fixed templates fail.
Validation against business rules and records
Extracted values are checked against your rules and existing records: totals that must reconcile, IDs that must match, dates that must fall in range. For BFSI clients we built a transformer-based validation model for regulatory accuracy.
Confidence thresholds and human-in-the-loop review
Every field carries a confidence score. Above the threshold it passes straight through; below it, a reviewer sees the document with the model's best guess pre-filled. Corrections feed back into training.
Domain models for claims and KYC
Medical NLP trained on healthcare terminology and abbreviations for claims documents, and extraction tuned for KYC, loan and financial statement packs in banking and insurance.
Integration and secure processing
Validated data is pushed into claims, core banking or logistics software through APIs, with a field-level audit trail. We have built pipelines to HIPAA and GDPR compliance standards.
How an engagement runs
Mine the document corpus
We sample your real documents to understand the distribution of layouts, languages, handwriting and edge cases before writing extraction logic, and agree target fields and accuracy per document type.
Design the pipeline
We design ingestion, classification, extraction, validation rules, confidence thresholds, the review interface and integration points as one pipeline, and set up a labelled test set to measure it.
Build and phase by document type
We start with the highest-volume document type, measure field accuracy against the test set, tune thresholds with your operations team, then add the next type.
Run, measure and retrain
In production we track straight-through rate, review queue size and field accuracy, and retrain on reviewer corrections so the share of documents needing a person keeps falling.
Ways to work with us
Fixed build / managed pod
We scope and deliver the pipeline end to end, phased by document type, with milestones, a senior delivery team, production handover and post-launch support.
Dedicated team
A squad of computer vision, ML and data engineers that works inside your roadmap to extend document AI across more document types and business units.
Individual experts
A computer vision engineer, AI/ML engineer or ML data engineer who joins your existing automation team on a monthly rolling basis.
Need individual engineers rather than a project? See roles and monthly pricing for IT staff augmentation.
Technology we work with
Case studies
Document Intelligence (BFSI)
Reads, classifies, extracts and validates documents end to end — with a human only where it matters.
Read the case study →Document AI · LogisticsIntelligent document processing
An OCR and machine-learning pipeline that reads bills of lading, invoices and shipping records — including handwritten fields — and feeds them into existing logistics software.
Read the case study →Frequently asked questions
What is the difference between IDP and OCR?
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.
When does a custom pipeline beat an off-the-shelf IDP tool?
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.
How do confidence thresholds and human review work?
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.
Can you extract data from handwritten documents?
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 national logistics operator we built extraction that captures handwritten fields on bills of lading, invoices and shipping records.
Do you handle KYC and loan document processing for banks?
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.
How do you use LLMs for document extraction without errors?
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.
How is data security handled for financial and health documents?
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.
Send us a sample document set
Share a handful of anonymised documents and the fields you need. We will come back within one business day with a view on extraction approach, review thresholds and where to start.