Manual data entry from documents
Teams key information from PDFs and scans into systems, which is slow and error-prone.
02 / Automation
Intelligent Document Processing
Documents still carry much of the information businesses run on: invoices, contracts, forms, claims, certificates and letters. Intelligent document processing turns them into structured, validated data, and shows reviewers exactly where the system is unsure.
At a glance
- Service
- Intelligent Document Processing
- Pillar
- Automation
- Typical starting point
- Document sample analysis
- Typical first phase
- 2 to 3 weeks for sample analysis; pilot in 6 to 10 weeks
- Key technologies
- Computer Vision, Natural Language Processing, Large Language Models
Durations depend on scope, data readiness and integration needs.
What is IDP?
Short answer
Intelligent document processing (IDP) is the automated classification, data extraction and validation of information from documents such as PDFs, scans, images and emails, using optical character recognition, layout-aware machine learning and language models, combined with business rules and human review for uncertain results.
Older OCR systems relied on fixed templates: they worked on one invoice layout and failed on the next. Current IDP uses models that understand document structure and language, so they can handle new layouts, handwriting to a degree, tables and multi-page documents.
Extraction accuracy is only part of the job. A production IDP system also classifies incoming documents, splits bundles, validates extracted values against business rules and master data, scores confidence field by field and presents uncertain fields to reviewers in a fast interface.
We measure accuracy at field level on your documents, not on vendor benchmarks, before recommending how much can be processed without review.
Business problems
What problems does IDP solve?
These are the situations where organisations most often find this service worthwhile.
Many layouts from many senders
Invoices, forms and certificates arrive in hundreds of formats, defeating template-based OCR.
Slow document-dependent processes
Onboarding, claims and payments wait until documents have been read and checked.
Missed information in long documents
Important clauses, dates or obligations are overlooked in lengthy contracts and reports.
Compliance checks on paperwork
Verifying that documents are complete, valid and consistent takes skilled time.
Archives that cannot be searched
Years of scanned records hold information nobody can find easily.
Use cases
Where can IDP be used?
Representative applications. The right use case depends on your processes, data and constraints.
| Area | Example application |
|---|---|
| Accounts payable | Invoice capture, line item extraction, PO matching and tax validation. |
| Banking and lending | Income proofs, bank statements, identity documents and application form processing. |
| Insurance | Claim forms, medical bills and supporting documents classified and extracted for adjusters. |
| Logistics | Bills of lading, delivery notes, customs and shipping documents. |
| Legal and contracts | Clause, party, date and obligation extraction for review and contract databases. |
| Education and government | Application forms, certificates and records digitisation with verification workflows. |
Who should consider it
- Organisations processing thousands of documents a month
- Finance, insurance and lending operations
- Teams with document-heavy onboarding or compliance checks
- Institutions digitising paper archives
How AARHIT approaches it
How we deliver IDP
Each stage ends with something you can review, so decisions are made on evidence rather than on promises.
Stage 1 of 6
Document sample analysis
We analyse a representative sample: document types, layouts, quality, languages and the fields that matter.
Output: document inventory, field list and a feasibility view on accuracy.
Stage 2 of 6
Ground truth preparation
A labelled test set is created with your reviewers so accuracy can be measured objectively.
Output: a validated test set for each document type.
Stage 3 of 6
Model selection and configuration
We compare pre-trained IDP services, open models and language models on the test set, then configure or fine-tune the best option.
Output: accuracy results by field and a recommended approach.
Stage 4 of 6
Validation and review design
Business rules, master data checks and confidence thresholds are defined, and a review interface is designed around the uncertain fields.
Output: validation rules and reviewer workflow.
Stage 5 of 6
Integration and pilot
Documents flow in from email, scanners or portals, and validated data flows into ERP, core systems or databases.
Output: a pilot processing live documents with measured results.
Stage 6 of 6
Monitoring and retraining
Reviewer corrections are captured, accuracy is tracked by field and document type, and models are updated when new layouts appear.
Output: accuracy dashboards and a retraining schedule.
Technology
What technologies are involved?
We choose tools on fit, cost, security and your existing estate. These are typical options, not fixed commitments.
Learn more about the underlying technologies
| Layer | Typical options |
|---|---|
| Capture | Email ingestion, scanner integration, mobile capture and portal uploads |
| OCR and layout | Azure AI Document Intelligence, Google Document AI, Amazon Textract or open-source OCR |
| Understanding | Layout-aware transformers, vision language models and LLM extraction with schemas |
| Validation | Rules engines, master data lookups and cross-document consistency checks |
| Review | Human-in-the-loop interfaces highlighting low-confidence fields |
| Integration | APIs and connectors to ERP, DMS, CRM and core business systems |
Outcomes
Potential business outcomes
Targets are agreed with you during scoping and measured against a baseline. We do not quote improvement figures before we have seen your process and data.
- Less manual keying and faster document turnaround
- Fewer data entry errors reaching downstream systems
- Consistent checks for completeness and validity
- Searchable, structured data from previously unstructured archives
How long does implementation generally take?
Typical timeline
Sample analysis and test set preparation usually take 2 to 3 weeks. A pilot on one or two document types commonly takes 6 to 10 weeks including integration. Adding document types afterwards is faster because the pipeline, review interface and integrations are reused.
What determines complexity?
- Number of document types and layout variety
- Scan quality, handwriting and languages
- Tables, stamps and multi-page structures
- Strictness of accuracy and compliance requirements
- Downstream system integrations
Implementation
Implementation considerations and risks
Most difficulties in this kind of work are predictable. Planning for them early keeps delivery on track.
Measure at field level
Overall document accuracy hides problems. A system can read 95 percent of fields correctly and still get the amount wrong.
Design the review experience
Reviewers should see the source image next to uncertain values and confirm or correct them in seconds.
Plan for Indian language and format variety
Regional languages, mixed scripts, GST formats and stamps need specific testing where relevant.
Retain source documents properly
Storage, retention and access policies must meet legal and regulatory requirements.
| Risk to consider | How we manage it |
|---|---|
| Incorrect values passing unnoticed | Field-level confidence thresholds, validation rules and sampling audits of automated results. |
| Personal data exposure | Encryption, access control, masking in review queues and compliant hosting choices. |
| New layouts reducing accuracy | Monitoring by sender or layout and a quick path to add examples and retrain. |
| Over-reliance on one vendor | Abstraction of the extraction layer so services can be compared or replaced. |
FAQ
Questions about IDP
How accurate is intelligent document processing?
Accuracy varies by document type, quality and field. Clean typed invoices often reach high field accuracy, while handwritten or poor scans are harder. We measure accuracy on a labelled sample of your documents before committing to any threshold.
Can IDP read handwritten documents?
Modern models can read many types of handwriting, but accuracy is lower and more variable than for printed text. Handwritten fields usually need human review unless testing shows otherwise.
Does IDP work with Indian documents and languages?
Yes, with appropriate model selection and testing. Hindi and several regional languages are supported by major OCR services and open models, and formats such as GST invoices can be handled with specific validation rules.
What is the difference between OCR and IDP?
OCR converts images of text into machine-readable characters. IDP builds on OCR by understanding which information is which, extracting it into structured fields, validating it and managing exceptions.
Can large language models replace IDP platforms?
LLMs and vision language models are very capable at extraction and are now part of many IDP solutions. They still need validation, confidence handling, cost control and review workflows around them, which is what an IDP system provides.
Next step
Considering IDP for your organisation?
Share the process, product or decision you have in mind. We will ask the right questions, outline options and suggest a sensible first step.