Trust fabric · National alignment

Built against SAHI — sutra by sutra.

India’s Strategy for Artificial Intelligence in Healthcare sets seven governing principles. Each maps to a property of the platform that can be tested, not to an intention.

01
Trust is the foundation

Provenance is written in the same transaction as the finding it describes. If provenance cannot be written, the finding is not persisted.

02
People first

Enforced, non-bypassable human sign-off. The model holds no authority; a named credentialed person does.

03
Innovation over restraint

Full device obligations assumed regardless of classification, so development proceeds without regulatory gambling.

04
Fairness and equity

Subgroup performance, calibration and refusal rate reported in every validation. Refusal rate by subgroup catches a safety feature becoming an access barrier.

05
Accountability

The signature binds a named, credentialed person to a content hash of exactly what was signed. Any change invalidates it.

06
Understandable by design

The criteria engine emits every criterion met and unmet, with the data that decided each. Two interactions to evidence.

07
Safety, resilience, sustainability

Fail-closed behaviour, drift monitoring, and a predetermined change control plan with automatic rollback.

BODH

Ready for independent benchmarking.

BODH — the Benchmarking Open Data Platform for Health AI, launched alongside SAHI at the India AI Impact Summit 2026 and developed by IIT Kanpur with the National Health Authority — is a federated environment under ABDM. Developers train models on-site without accessing raw patient data; only refined model weights return. It assesses performance, robustness, bias and generalisability against diverse, anonymised Indian datasets.

That is a description of the architecture we already committed to. A platform that centralises data cannot enter that process; one that is federated, standards-native and reproducible by construction can enter it immediately.

Federated evaluationFederated compute is a platform property, not a deployment option — models move to data.
No raw data egressOnly derived or aggregate results leave the custodian environment.
Bias and generalisabilitySubgroup performance, calibration and refusal rate are already release criteria, reported favourable or not.
RobustnessFail-closed behaviour verified against a deliberately degraded input corpus.
Third-party auditProvenance makes any historical result reproducible for an external auditor.

IIT Kanpur has described BODH as having the potential to become a default third-party algorithmic auditing tool for health AI. Soma Nova intends to submit for benchmarking rather than avoid it.

ABDM

Aligned to the national rails.

ABHA identityResolved to the internal twin identity; the internal identifier never leaves the boundary.
RegistriesFacility and practitioner registries used for site verification and signer credentialing.
Consent managerNational consent honoured alongside the platform’s own purpose-bound model.
Health information exchangeRecord linkage and retrieval through defined national pathways.
Data standardsFHIR-native, applied as contract rather than adapter.
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Discuss national deployment.

For programmes, ministries and public-health bodies working within ABDM.