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Work / Bharat-First / Vaidya-Niti

No. 12 · Bharat-First · Healthcare

Where clinical truth
meets digital trust. वैद्य-नीति · Vaidya-Niti

Indian hospitals lose 15 to 20% of revenue to claim rejections caused by clerical errors. Vaidya-Niti reads the chart, reasons over the policy and tells the clinician what is missing, in seconds. Indigenous AI first. DPDP-compliant. Mumbai-resident. Sovereign by design.

View source How it works Proof
71Tests passing
22Indian languages
80+Slang terms mapped to ICD-10
20+AYUSH-allopathic interactions
4Indigenous AI providers, in priority

Act I · The Diagnosis

India's hospitals are bleeding in three places.

Existing systems are filing cabinets. They store data. They do not reason over it. They are not multilingual. They are not sovereign. They are not affordable for the half-million small clinics that hold the country up.

80%

Dark data

Clinical history is locked inside voice notes, handwritten prescriptions and unstructured discharge summaries. None of it queryable.

15-20%

Revenue loss

Small hospitals lose this much of every claim cycle to clerical rejections. The patient pays out of pocket. Trust dies.

DPDP

Privacy paralysis

The DPDP Act 2023 mandates strict handling of patient data. No affordable, India-resident tooling exists for clinics that need it most.

Act II · The Promise

Catch the rejection before the rejection.

Insurers reject claims weeks after submission. The patient is already discharged, the bill already disputed. Vaidya-Niti adjudicates the claim against the policy at the moment it is written, returning a Claim Confidence Score with the exact gaps to fix.

Without Vaidya-Niti

The 21-day rejection loop

  • Bill clerk submits claim. Files leave the hospital.
  • Insurer reviews on day 7 to 21. Spots a missing pre-auth note.
  • Claim returns rejected. Patient is asked to pay in cash.
  • Hospital appeals. Two more weeks. 40% never recovered.
With Vaidya-Niti

The 8-second loop

  • Clinician dictates in any of 22 Indian languages. Scribe agent writes a FHIR R4 record.
  • Adjudicator reads the record against the patient's exact policy and the standard treatment guideline.
  • Claim Confidence Score returns with named gaps: missing ECG, sublimit on stent, pre-auth not raised.
  • Clinician fixes the gaps. Submits clean. First-pass approval.

Act III · The Scribe

Voice in. Structured medical record out.

A six-step pipeline turns a clinician's spoken note into a FHIR R4-compliant record, then reads the structured record back hands-free for confirmation. No keyboard. No retyping.

  1. 01

    Ingest

    Audio captured from microphone, phone or upload. Encrypted at rest with AES-256-GCM the moment it lands.

  2. 02

    Speech-to-text, indigenous-first

    Sarvam Bulbul tries first. Bhashini next. Krutrim after that. Gemini 3 Flash only as a backstop.

  3. 03

    Transliterate to medical English

    "Sugar ki bimari" becomes E11. 80+ regional colloquialisms across Hindi, Tamil, Telugu, Bengali, Marathi and Gujarati map to ICD-10 codes.

  4. 04

    Extract clinical entities

    Diagnosis, line of treatment, procedure, medication, dose. AYUSH-allopathic interactions checked against 20+ known severities.

  5. 05

    Build the FHIR R4 resource

    Pydantic v2 + fhir.resources validates the structure. Adjudicator agent tests it against the patient's exact insurance policy.

  6. 06

    Speak the record back

    TTS reads the structured note in the clinician's own language. They confirm or correct, hands-free, eyes on the patient.

Act IV · The Cascade

Indian AI first. Always.

Every capability · speech, translation, vision, reasoning · has its own ordered provider list. The cascade only advances when an indigenous provider is unavailable, returns an error or has no key configured. Foreign infrastructure is the backstop, never the default.

1Primary

Sarvam AI

Bharat · IndiaAI Mission partner

Bulbul v3 for TTS. Sarvam Vision 3B for OCR. Sarvam-M for chat. Built for Indian accents and scripts.

2Secondary

Bhashini

Govt. of India · ULCA

National language platform. ASR, NMT and TTS across all 22 scheduled Indian languages. Free, government-backed.

3Tertiary

Krutrim

Ola · India's first AI unicorn

Indigenous LLM and translation. OpenAI-compatible interface. Strong Indic coverage with sovereign hosting.

4Backstop

Gemini 3 Flash

Vertex AI · asia-south1 only

Activated only when all three indigenous providers fail. Geo-fenced to Mumbai. Pay-per-token, by exception.

Act V · Proof

It already passes.

Test Suite
71 tests across adjudicator, API, AYUSH interactions, medical slang, security and vector store.
All pass
Region
Geo-fenced to GCP asia-south1 (Mumbai). No data egress. DPDP Act 2023 honored end-to-end.
Mumbai
Encryption
AES-256-GCM for PHI at rest. TLS 1.3 in transit. PII masking at the logging boundary.
256-bit
FHIR
Every clinical record validated against fhir.resources R4 schema. Pydantic v2 strict.
R4
Hackathon
Built for the Economic Times Gen AI Hackathon 2026. Full architecture, pitch deck and seven live API endpoints.
2026
Roadmap
Production target: Yotta Shakti Cloud Confidential VMs (AMD SEV-SNP). Even the cloud provider cannot read PHI in use.
SEV-SNP

The Stack

Production-grade. Sovereign-hosted. Indian by default.

  • Python 3.12+
  • FastAPI
  • Pydantic v2
  • httpx async
  • Sarvam AI
  • Bhashini
  • Krutrim AI
  • Gemini 3 Flash
  • FHIR R4 (fhir.resources)
  • aiosqlite
  • ChromaDB
  • AES-256-GCM
  • Bandit + ruff
  • CycloneDX SBOM
  • GCP asia-south1

If a clinic in Bilaspur can run it, your hospital chain can too.

I build sovereign, multilingual, FHIR-native AI for Indian healthcare. If you run a hospital chain, an insurer or a public-health programme, this is the engineer to call.

Hire me Back to work