Intelligent Document Processing Services
OCR can read characters. Operations still need to know what the document is, which fields matter, whether those fields agree, and where the result belongs. RaftLabs develops intelligent document processing systems for one defined document flow, including intake, classification, extraction, validation, exception review, and delivery.
Bring the problem, the current workflow, or the existing code. We reply with a practical next step within one business day.
Evidence and scope
20,000+ transactions
AI OCR proof
Gas-station platform processed live operational volume during testing.
$30K
Starting scope
One document type, review queue, and one destination.
Field level
Control model
Low-confidence fields stop for human review.
The brief
Good software decisions begin with the constraint, not a list of features or a preferred technology.
Are people retyping the same fields from invoices, forms, receipts, or claims into another system?
Do extraction errors travel downstream because nobody sees the uncertain field?
Plain answer
Intelligent document processing classifies files, extracts named fields, validates results, and sends exceptions for review. RaftLabs develops IDP workflows around real samples and downstream systems, with accuracy measured by required field. One workflow for a document type starts at $30,000.
A total is read correctly but assigned to tax. A date is extracted but parsed in the wrong order. A supplier name is close enough to pass a visual scan and wrong enough to create a duplicate record.
Intelligent document processing turns an image into trusted data only after classification, extraction, validation, and review agree. RaftLabs builds that full path into the operation that consumes the result.
Proof
Start with one document type. A vague promise to process every document hides the layouts and exceptions that decide quality.
People repeatedly read the same document type and enter defined fields into another system.
You can provide representative samples, including poor scans and uncommon layouts.
A process owner can define field-level tolerance, review rules, and the system of record.
The documents are rare, highly bespoke, and require expert interpretation throughout.
No representative samples can be used for evaluation.
The downstream process has no stable fields, validation rules, or accountable reviewer.
Scope
| OCR | Intelligent document processing | |
|---|---|---|
| Primary job | Convert an image of text into characters | Turn a document into validated structured data |
| Document type | Often supplied by the caller or template | Classified before the correct rules run |
| Quality unit | Character or word recognition | Required field accepted at the agreed error boundary |
| Exceptions | Raw confidence or output | A review queue with source, reason, and correction |
| Destination | Text returned to the caller | Accepted data reconciled with a system of record |
OCR may be enough when searchable text is the outcome. IDP earns its place when the next system needs named, checked fields and the operation needs to account for uncertain ones.
Choose OCR development when recognition itself is the deliverable. Choose workflow automation when the documents are already structured and the main problem is routing work between systems.
How it works
The benchmark uses the documents your team actually receives, not a clean demonstration set.
Collect representative files across layouts, sources, image quality, languages, and edge cases. Name every required field and the downstream consequence of an incorrect or missing value.
Compare extraction approaches on held-back samples. Measure each required field, not one blended accuracy number. Define thresholds for automatic acceptance, rejection, and human review.
Add format and business checks, source lookups, duplicate detection, reviewer permissions, and correction history. The reviewer sees the document and flagged field together.
Deliver accepted data to one destination and reconcile its status. Add new layouts or document types only after the first workflow meets its field-level benchmark in production.
RaftLabs built a gas-station management platform with AI OCR that connected to existing point-of-sale systems. During testing, it processed more than 20,000 transactions in one day; 40+ stations joined the beta rollout.
That proof belongs to the tested platform and document mix. It is not a universal IDP accuracy or throughput promise. A new workflow begins with its own samples, fields, integrations, and acceptance threshold.
Scope and price
The first scope covers representative samples, named fields, extraction, validation, exception review, and one integration.
More document types, languages, handwriting, tables, throughput, and destination systems belong in later phases after the first benchmark holds.
Starting investment
Starts at $30,000
A focused workflow commonly takes 8 to 14 weeks. Layout variation and field-level error tolerance move the estimate most.
Benchmark before scale
The first phase reports quality by required field on held-back samples. We do not turn a demo accuracy figure into a production promise.
Fixed-price phase
Once the sample, fields, review path, and destination are agreed, the phase price is locked in writing.
Useful next steps
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Multi-location gas station operator processes 20k+ daily transactions after replacing spreadsheets with AI OCR software
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Read moreIntelligent document processing, or IDP, turns PDFs, scans, images, forms, or email attachments into validated structured data. A complete workflow covers intake, document classification, field extraction, business-rule checks, low-confidence review, correction, and delivery into another system.
OCR converts an image of text into characters. IDP identifies the document, extracts named fields, validates those values, and sends uncertain results to review before routing accepted data. OCR can be one component inside an IDP workflow.
Accuracy depends on the document mix, image quality, layouts, languages, handwriting, required fields, and tolerance for error. We do not promise a universal percentage. We benchmark each required field on representative samples and define a review threshold before production scope is approved.
The workflow should stop the affected field or document from reaching the destination automatically. A reviewer sees the source image, extracted value, reason for the flag, and relevant checks, then confirms or corrects it. That correction becomes part of the audit record and future evaluation set.
A focused system for one document type, one review queue, and one downstream integration starts around $30,000. Cost grows with layout variation, handwriting, languages, table extraction, validation rules, throughput, retention, user roles, and the number of destination systems.
Work with us
We will choose a representative sample and define the first field-level benchmark, review path, and destination.