Empower your document operations with AI.
OCR gets you characters. Agents get you the answer — the right page out of hundreds, read even when the scan is poor, related to everything else in the file, and scored so you know what to trust.
All of it inside your own cloud — your Azure tenant, your keys, your data boundary.
Not your cleanest one. The blurred, stamped, handwritten one that your current tool returns nonsense for.
This is for you if
If none of these are true we are probably not the right call yet, and we will say so.
- Bundles arrive with far more pages than the ones you need
- Scans, phone photographs and handwriting break your current OCR
- Documents come in more than one language, sometimes on one page
- Values have to be reconciled against another page or another system
Where documents actually go wrong
Select a stage to see the difference on real documents.
Scroll until you find page 47
A 180-page bundle arrives and three pages in it matter. Someone opens it and scrolls, every time, for every bundle — and the person who knows which pages matter is the bottleneck.
Every page classified on arrival
The bundle is split and each page identified by what it actually is — invoice, annexure, ID proof, correspondence, blank separator. The pages that matter are pulled out and the rest are left alone.
Clean documents work. Real ones don't
A phone photograph taken at an angle, a fourth-generation photocopy, a form filled in by hand and stamped across the total. Traditional OCR returns confident nonsense, or nothing at all.
Built for the documents you actually get
Skew, shadow, noise and low resolution are corrected before reading. Printed and handwritten text, stamps, signatures, ticked boxes and multi-page tables are read across 20+ languages, including mixed-script and Indic-language documents.
The check happens by eye, or not at all
The amount on page 12 is supposed to match the purchase order on page 47 and the contract signed last year. Whether anyone actually checks depends on how busy the day is.
Fields linked to each other, and to your systems
Values are cross-referenced across pages within the bundle and against the records you already hold. Where a document disagrees with a system of record, the disagreement is surfaced as the finding — not averaged away.
Everything checked the same, so nothing is
A four-page invoice and a ninety-page claim file get the same cursory review, because there is no signal for which one deserves attention.
You set the threshold, per field
Every field carries a confidence score. Above your threshold it releases straight into your systems; below it, only that field goes to a person — with the source image cropped to the exact region it came from.
Clean documents work. Real ones don't
A phone photograph taken at an angle, a fourth-generation photocopy, a form filled in by hand and stamped across the total. Traditional OCR returns confident nonsense, or nothing at all.
Built for the documents you actually get
Skew, shadow, noise and low resolution are corrected before reading. Printed and handwritten text, stamps, signatures, ticked boxes and multi-page tables are read across 20+ languages, including mixed-script and Indic-language documents.
Language is not a feature flag here. Hindi, Marathi, Tamil, Bengali, Gujarati, Arabic and mixed English-Indic documents are read natively — including where two scripts appear on the same page, in the same table, with the source region retained beside every extracted value.
What a document team is judged on.
Every field carries a confidence score and a crop of the source region it came from, so an extracted value can always be traced back to the pixels that produced it.
Clean fields release without review. The proportion is yours to set, by tuning the threshold per field rather than accepting one number across the whole document.
A bundle becomes structured, cross-checked records on arrival, rather than after it reaches the top of someone's queue.
The documents you actually get.
Every vendor handles a clean, single-language, machine-generated PDF. The value is in what happens to everything else.
Bundles split and classified page by page, so the relevant pages surface without anyone scrolling.
Skew, shadow, glare and low resolution corrected before a single character is read.
Handwritten entries, ticked boxes, initials and margin notes read alongside the printed text.
Overlapping stamps, seals and signatures separated from the underlying value.
Rows reassembled across page boundaries, with headers carried forward correctly.
Mixed English-Indic and other multi-script documents read natively, without a per-language pipeline.
In production
You can see exactly how it is behaving.
Straight-through rate, why documents escalate, how accuracy is moving and where the reviewer queue is building — reported continuously, not assembled for a quarterly review.
- Low field confidence54%
- Unknown document type21%
- Conflicting sources15%
- Above value threshold10%
Drift — claim-form accuracy down 2.1pp this week. A new layout is in circulation; threshold raised automatically until it is reviewed.
| Document type | In queue | Median review | Auto-release |
|---|---|---|---|
| Invoices | 12 | 38s | 96% |
| Claim forms | 31 | 1m 12s | 88% |
| KYC packs | 7 | 55s | 93% |
| Shipping docs | 4 | 41s | 97% |
An illustrative view of the review console. Figures are sample values showing what is reported, not results from a specific engagement.
It runs in your Azure account.
The documents are the sensitive asset. So the pipeline goes to them — installed inside your tenant, under your governance — rather than the documents being shipped to us.
Which is also the shortest route through your own security review: there is no new data-transfer agreement to negotiate, because no data transfer happens.
Deployed into your own tenant — Azure AI Document Intelligence and Azure OpenAI running under your account, not ours.
No copies on our infrastructure, no third-party API in the path, nothing retained outside your boundary.
Key management, retention windows and deletion policy stay under your control and your existing governance.
Private endpoints and VNet integration, with no public egress required for the pipeline to run.
Entra ID, your RBAC roles and your conditional access — reviewers see only what their role permits.
The same pipeline runs on AWS, on-premise, or fully air-gapped where documents cannot touch a public cloud.
Let's talk about the bundles that slow you down.
Not the tidy case — the bundles that break what you have now. Describe them on a call and we will tell you how they would be handled, and what it takes to run it inside your own Azure tenant.
Document processing — common questions
Conventional OCR converts a page into characters. It cannot tell you which of 180 pages matters, it degrades badly on photographs and handwriting, and it has no way to check the amount on one page against the purchase order on another. Idea Infotech pairs OCR with agents that locate the relevant pages, read difficult ones, relate values across the bundle and against your systems, and score each field so you can decide what releases automatically.
More than twenty, including Hindi, Marathi, Tamil, Bengali, Gujarati and Arabic alongside English, and including documents where two scripts appear on the same page or in the same table. Language does not need to be declared in advance or routed to a separate pipeline.
Yes, and that is the usual deployment. The pipeline is installed into your own Azure subscription, using Azure AI Document Intelligence and Azure OpenAI under your account, your keys and your networking. Documents are not copied to Idea Infotech infrastructure and no third-party API sits in the processing path. AWS, on-premise and fully air-gapped deployments are also supported.
By setting confidence thresholds, per field rather than per document. A field above its threshold releases straight into your systems; a field below it is routed to a reviewer with the source region cropped to the exact area it was read from. Thresholds can start conservative and be relaxed as measured accuracy earns it.
It is not guessed at. An unrecognised document class is escalated rather than force-fitted to the nearest known template, and once a reviewer has classified it the handling can be promoted so the same type is recognised next time.