Production AISystems
Knowledge and Automation

Document AI Solutions

Document AI that extracts structured data from invoices, forms, contracts, IDs and reports using OCR, classification and field extraction. Validated against business rules, reviewed by humans when confidence is low and exported to your business systems. Built for high-volume document processing in production.

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What are document AI solutions?

Document AI solutions use optical character recognition, document classification and field extraction to turn unstructured documents—invoices, forms, contracts, IDs, reports—into structured data that your business systems can use. The system reads the document, identifies its type, extracts the relevant fields and tables, validates them against business rules and routes low-confidence extractions to a human reviewer. The output is clean, structured data exported to your ERP, CRM or database. It is measured on field-level accuracy, processing time and review rate.

Manual data entry is slow and error-prone

People read documents and type data into systems by hand. Document AI extracts fields automatically with higher accuracy.

Document types and layouts vary

Every invoice, contract and form looks different. Document AI classifies and adapts to different layouts.

Tables and complex structures are hard to extract

Line items, nested tables and multi-page documents. Document AI handles these with layout understanding.

Extracted data needs validation

Wrong amounts, missing fields and invalid dates. Document AI validates against business rules before export.

How This Solution Can Be Built

Different situations call for different implementations. These are the most common variants.

Invoice processing

Extract vendor, date, line items and totals from invoices. Validate against POs and route for approval.

Form processing

Extract fields from structured and semi-structured forms—applications, claims, intake documents.

Contract analysis

Extract parties, dates, obligations and risks from contracts. Compare against playbook.

ID and KYC processing

Extract and verify information from identity documents—Aadhaar, PAN, passports, driver licences.

How the System Works

From input to output, each step is engineered for a specific purpose.

1

Document received

Document arrives via upload, email, API or scan

input
2

OCR and layout

Text and layout extracted from the document image or PDF

ai
3

Classification

Document type identified—invoice, contract, form, ID

ai
4

Field extraction

Key fields and tables extracted based on document type

ai
5

Validation

Extracted data validated against business rules—amounts, dates, required fields

control
6

Confidence check

Low-confidence fields are flagged for human review

control
7

Human review

Reviewer checks flagged fields, corrects if needed and approves

human
8

Export

Validated data exported to ERP, CRM or database

output

Production and Enterprise Readiness

What makes this solution work in a real operating environment—not just in a demo.

Confidence-based review

Fields below a confidence threshold are routed to human review. The threshold is configurable per field type.

Validation rules

Business rules validate extracted data—amount ranges, date formats, required fields, duplicate checks.

Multi-page and table support

The system handles multi-page documents, nested tables and cross-page relationships.

Audit trail

Every extraction, correction and approval is logged for compliance.

Integration

Export to ERP, CRM, database or API. Custom integrations are built as needed.

How Success Is Measured

The right metrics depend on the solution. These are the measures that matter for this system.

Illustrative system view — metrics shown in the dashboard above are labelled examples, not client results.

Field-level accuracy

Percentage of fields extracted correctly without human correction

Document processing time

Time from document receipt to validated export

Review rate

Percentage of documents or fields that require human review

Validation failures

Percentage of documents that fail validation rules and need correction

Cost per processed document

Total cost divided by processed documents, including OCR, extraction and review

View related case studies

Discuss a Document Workflow

Tell us the workflow, problem or system you want to improve. We respond with how we would approach it.

Discuss a Document Workflow

No finished technical specification required.

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Frequently Asked Questions

What types of documents can it process?
Invoices, forms, contracts, IDs, receipts, purchase orders, claims, reports and most structured or semi-structured business documents. The system classifies the document type and applies the right extraction model. For document types it has not seen before, a new extraction configuration is created.
How accurate is the extraction?
Field-level accuracy depends on document type and layout consistency. Structured forms typically achieve above 95% field accuracy. Unstructured documents like contracts are lower. Low-confidence fields are routed to human review, so the effective accuracy after review is higher. Accuracy is measured per field type, not as a single number.
Can it handle tables and line items?
Yes. The system uses layout understanding to extract tables, including nested tables and multi-page tables. Line items in invoices, schedules in contracts and data tables in reports are extracted as structured rows. Table structure—columns, rows, headers—is preserved.
How does human review work?
Fields below a confidence threshold are flagged. A reviewer sees the document image, the extracted value and the confidence score. The reviewer corrects the value if needed and approves. Corrections are logged and used to improve the extraction model over time. The review threshold is configurable per field type.
Can it export to our ERP or CRM?
Yes. Validated data is exported to your ERP, CRM, database or API. Common integrations include SAP, Oracle, Salesforce, Dynamics and custom databases. The export format and destination are configured during implementation.
How does it handle different languages and layouts?
OCR supports multiple languages. The extraction model is trained on your document layouts—different invoice formats, contract templates and form designs. For new layouts, the system adapts with minimal additional training. Layout understanding preserves the spatial relationships between fields.