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.

Your tenant

Runs in Azure

20+

Languages

You control

Confidence thresholds

How it reads

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.

Where documents actually go wrong

Ask the harder questions.

Select a stage to see the difference on real documents.

TodayNow

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.

The exceptions become a manual queue that never shrinks

With OCR + agentsNext

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.

Difficult pages get read instead of getting queued

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 you are judged on

What a document team is judged on.

Accuracy you can evidence

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.

Straight-through rate

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.

Time to usable data

A bundle becomes structured, cross-checked records on arrival, rather than after it reaches the top of someone's queue.

Hard cases

The documents you actually get.

Every vendor handles a clean, single-language, machine-generated PDF. The value is in what happens to everything else.

The pages you need are 3 of 180

Bundles split and classified page by page, so the relevant pages surface without anyone scrolling.

Photographed at an angle, in bad light

Skew, shadow, glare and low resolution corrected before a single character is read.

Filled in by hand

Handwritten entries, ticked boxes, initials and margin notes read alongside the printed text.

Stamped across the figure that matters

Overlapping stamps, seals and signatures separated from the underlying value.

Tables that break across pages

Rows reassembled across page boundaries, with headers carried forward correctly.

Two scripts on the same page

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.

Documents processed

48,210

last 30 days

Straight-through

91.4%

released without review

Escalated

8.6%

routed to a person

Median time to data

42s

arrival to system of record

Disposition of every documentlast 30 days

91.4% released

8.6% escalated

Why it escalated

Low field confidence54%
Unknown document type21%
Conflicting sources15%
Above value threshold10%

Field-level accuracy98.5%

sampled against reviewer corrections, 14 days

Drift — claim-form accuracy down 2.1pp this week. A new layout is in circulation; threshold raised automatically until it is reviewed.

Review queue by document type

Review queue by document type, showing items awaiting review, median review time and auto-release rate.
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.

Deployment

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. There is no new data-transfer agreement to negotiate, because no data transfer happens.

Your Azure subscription

Deployed into your own tenant — Azure AI Document Intelligence and Azure OpenAI running under your account, not ours.

Documents never leave

No copies on our infrastructure, no third-party API in the path, nothing retained outside your boundary.

Your keys, your retention

Key management, retention windows and deletion policy stay under your control and your existing governance.

Private networking

Private endpoints and VNet integration, with no public egress required for the pipeline to run.

Your identity model

Entra ID, your RBAC roles and your conditional access — reviewers see only what their role permits.

Or somewhere else entirely

The same pipeline runs on AWS, on-premise, or fully air-gapped where documents cannot touch a public cloud.

This is for you

This is for you if

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

If none of these are true we are probably not the right call yet, and we will say so. Not the tidy case — the bundles that break what you have now. Describe them and we will tell you how they would be handled inside your own Azure tenant.

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