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Yira.ai

Health data intelligence

Every medical document. A clearer claims decision.

Medical document intelligence for insurers, TPAs and hospitals: AI that reads medical records and hospital tariffs, verifies identity and flags suspicious claims.

1 lakh+documents parsed
5,000+fraud cases flagged
100+insurance operations
MedSense · Lab reportParsed
Bilirubin
ALT (SGPT)52.19 U/L
AST (SGOT)
ALP236 U/L
Albumin
Extracted fields
ALT52.19 U/LHigh
ALP236 U/LHigh
Facial recognition

Confidence score

Prior records

16 / 17 fields matched

4 prior matchesSent for review
JSON readyPushed to claims system
Trusted in production byHealth Assist Insurance TPA Pvt Ltd
ISO 9001:2015SOC 2HIPAAGDPRFTCCI member
How it works

Medical document intelligence, from raw document to integrated data.

A four-stage AI pipeline behind every claim.

1
Ingest

Upload any way

API, mobile app or bulk scanner portal.

Scanned PDFsHandwritten notesLab reportsMobile photos
2
Understand

Read the medicine

Medical-grade OCR and NLP identify entities.

ICD-10 codesDrug names (NLEM)Patient demographicsBilling line items
3
Validate

Check and standardise

Formatted for downstream systems.

Smart validationReview checksStandardisationStandard formats
4
Integrate

Push to your platforms

Straight into the systems you use.

TPA portalsInsurance systemsHospital EMRsAnalytics DB
See the pipeline run on your own documents.Book your demo today
The platform

Three layers of intelligence. See what each one returns.

01 · Extract

Documents in. Structured data out.

Medical histories, lab reports and tariffs become JSON, clinical fields and rate tables.

History
Lab
Tariff
{ "test": "ALT", "value": 52.19, "unit": "U/L", "flag": "High" }
02 · Verify · Facial recognition

Facial recognition: is this the same patient?

Facial recognition compares the ID photo with the hospital image and scored, alongside a comparison of medical reports.

ID photo
Confidence score
Hospital
Lab values

Flagged against the report's range

ALT
52.19 U/L
ALP
236 U/L
Report rangeResult
Medical history

Handwriting to structured answers

Q
A
Q
A
03 · Surface · Fraud detection

Fraud detection with prior-record matching

16 / 17 fields
4 prior matches for review

Sample outputs. Confidence scores support reviewers and are not validated accuracy; a record match is a signal for investigation, not a fraud determination.

See these outputs on your claims documents.Book your demo today
Case study · Hospital tariffs

Read the tariff. Apply the rule.

Tariff sheets become procedure-level data
Contract rules applied per room category
Missing rates stay visible as NA
Book your demo today
Case study: cataract procedureIllustrative contract · INR
−50%₹8,500
−25%₹12,750
Base₹17,000
+25%₹21,250
NA
GeneralTwin sharingPrivateDeluxeNo listed rate

Rules are contract-specific.

Who it's for

Built for insurance operations.

Buyers

Insurers and TPAs

Claims data, identity checks and fraud signals in one platform.

Daily users

Claims teams and medical officers

Less retyping. The decision stays with them.

Also for

Hospitals

Tariffs and records in a form payers can use.

JSON and APIsClient-managed deploymentHuman in the loop
Built for your claims team. See it on your documents.Book your demo today
Rollout

Start with one workflow.

4–5weeks to go live, indicative

Sandbox

Agree documents, fields and review criteria.

Provision

Tenant, access and API credentials.

Integrate

Connect outputs to your claims workflow.

Go live

Run in parallel, monitor exceptions.

Subject to scope, data access and integration readiness.

Your first workflow can start with a demo today.Book your demo today
Security and compliance

Built for sensitive health data.

Compliance documents shared during evaluation. Client-managed deployment when data must stay with you.

ISO 9001:2015Certified
SOC 2Compliant
HIPAACompliant
GDPRCompliant
FAQ

Questions from claims teams

Which documents can MedSense read?

Medical histories (including handwritten), lab reports and hospital tariffs, as scans, PDFs or mobile photos.

Does MedSense decide if a claim is fraudulent?

No. It surfaces matches, identity scores and inconsistencies for your investigators. Every decision stays with your team.

How does it connect to our systems?

Structured JSON through APIs, pushed to your TPA portal, insurance system, hospital EMR or analytics database.

Can it run in our own environment?

Yes, client-managed deployment is available. We agree the set-up during the pilot.

How long does a rollout take?

About 4–5 weeks for one workflow, depending on scope, data access and integration readiness.

How do we get started?

Book your demo today. We run MedSense on the documents and fields that matter to your claims team, then agree a scoped pilot.

Get started

Book your demo today.

See MedSense on the documents and fields that matter to your claims team.