Sarvam AI

frontier_builder · India , Bengaluru · founded 2023

Employees: 51-200 · Stage: Series A · Funding: $391,000,000

48.9

elevated

default-balanced
composite score

Indian sovereign AI platform building open-weight multilingual models (Sarvam-M, OpenHathi) optimized for the 10 most-spoken Indic languages. Selected by IndiaAI Mission as the first Indian-funded foundation model partner. Targets BFSI, healthcare, and government deployments inside India.

Analyst summary

Sarvam is the most credentialed of the Indian sovereign-AI builders: SOC 2 Type II, ISO 27001, and DPDP compliance are in place, the IndiaAI Mission selected it as the first home-grown foundation-model partner, and the April 2026 funding round brought Nvidia, Amazon, Bessemer and Accel onto the cap table at a roughly 14x markup in under 30 months. The model lineup — Sarvam-M, OpenHathi — is open-weight on Hugging Face and tuned for the top ten Indic languages, which is the differentiator for buyers in BFSI, healthcare and government inside India.

Most mature of the Indian sovereign-AI bets. Compliance posture is enterprise-grade for an Indian-jurisdiction deployment; the obvious gaps are Western privacy frameworks (GDPR / HIPAA) and the implicit government-alignment risk that comes with the IndiaAI partnership. For non-India deployments the case is weaker than a US or EU peer.

Rating: recommended

Compliance posture

SOC 2 Type II	Yes (report 2026-02-01)
ISO 27001	Yes
ISO 42001 (AI management system)	No / not disclosed
FedRAMP authorized	No / not disclosed
GDPR compliant	No / not disclosed
CCPA compliant	No / not disclosed
HIPAA compliant	No / not disclosed
NIST AI RMF aligned	No / not disclosed
CSA STAR certified	No / not disclosed
EU AI Act classification	not_applicable

Data handling

Trains on user data	opt_out_default
Outputs feed model improvement	opt_out_default
Data retention period	Configurable per plan; on-premise option available
Can delete user data on request	Yes (SLA 30 days)
Default data residency	IN
Encryption at rest	Yes (AES-256 / TLS 1.2+)
Encryption in transit	Yes
DPA available	Yes
Public subprocessor list	No / not disclosed
HIPAA BAA available	No / not disclosed

IP profile

User owns outputs	yes
Vendor claims output rights	No / not disclosed
Input IP protection	standard
Indemnification offered	No / not disclosed
Copyright shield program	No / not disclosed
Commercial use permitted	Yes
Training data provenance	mixed_sources
Known IP lawsuits	No / not disclosed

Jurisdiction

Incorporation country	IN
Incorporation jurisdiction risk	moderate
Subject to US jurisdiction	No / not disclosed
Subject to EU jurisdiction	No / not disclosed
Subject to China jurisdiction	No / not disclosed
Subject to Russia jurisdiction	No / not disclosed
Government data access risk	moderate
Five Eyes aligned	No / not disclosed
Adequate privacy jurisdiction	No / not disclosed

Governance

Publishes model cards	Yes
Publishes transparency reports	No / not disclosed
Has AI ethics board	No / not disclosed
Safety testing disclosed	Yes
Red-teaming program	No / not disclosed
Government contracts	Yes
Terms of service	https://www.sarvam.ai/terms
Privacy policy	https://www.sarvam.ai/privacy

Incidents on record

No incidents on file.

OWASP LLM Top 10 cross-walk

TrustAtlas dimensions that materially address each OWASP risk. Use to translate this vendor's compliance posture and data-handling stance into the application-security vocabulary your security team already uses.

LLM01	Prompt Injection User-supplied prompts manipulate model behaviour to bypass intended controls. SecurityTransparencyDependency chain
LLM02	Sensitive Information Disclosure Models leak PII, PHI, secrets, or proprietary data through outputs. Data handlingIP exposureJurisdiction
LLM03	Supply Chain Risk propagates from upstream models, datasets, plug-ins, and vendors. Dependency chainBusiness stabilitySecurity
LLM04	Data and Model Poisoning Adversarial training data or fine-tuning input degrades model integrity. Data handlingTransparencySecurity
LLM05	Improper Output Handling Downstream systems blindly trust model output, enabling injection downstream. IP exposureTransparency
LLM06	Excessive Agency Agents granted overbroad tool, identity, or permission scopes cause harm. Dependency chainTransparencyJurisdiction
LLM07	System Prompt Leakage System prompts containing secrets or logic are extracted via crafted input. Data handlingTransparency
LLM08	Vector and Embedding Weaknesses Vector stores and RAG pipelines leak or contaminate retrieved context. Data handlingSecurity
LLM09	Misinformation Hallucinated, biased, or fabricated outputs treated as authoritative. TransparencyRegulatory complianceBusiness stability
LLM10	Unbounded Consumption Cost, denial-of-service, and resource-exhaustion attacks against LLM endpoints. SecurityBusiness stability

Full framework reference: https://trustatlas.pages.dev/framework/owasp-llm-top-10

NIST AI RMF cross-walk

How each NIST AI RMF function is supported by the dimensions TrustAtlas scores.

GOVERN	Govern Establish AI governance structure: policies, roles, accountability. Regulatory complianceJurisdictionTransparencyBusiness stability
MAP	Map Establish AI context: intended purpose, use cases, capabilities, and risks. TransparencyDependency chainData handlingIP exposure
MEASURE	Measure Quantitative + qualitative risk assessment: testing, benchmarks, monitoring. SecurityData handlingTransparency
MANAGE	Manage Treat identified risks: mitigation, controls, incident response, lifecycle. Regulatory complianceSecurityDependency chainBusiness stability

Full framework reference: https://trustatlas.pages.dev/framework/nist-ai-rmf

Cited sources

Field	Source
governance.government_contracts	https://restofworld.org/2026/india-frugal-ai-sarvam-krutrim-sovereign/ Verified 2026-03-12 by analyst
governance.open_source_contributions	https://www.sarvam.ai/ Verified 2026-05-15 by analyst
security_compliance.iso_27001	https://www.sarvam.ai/about-us Verified 2026-05-15 by analyst
security_compliance.soc2_type2	https://www.sarvam.ai/about-us Verified 2026-05-15 by analyst
vendors.founded_year	https://en.wikipedia.org/wiki/Sarvam_AI Verified 2026-05-10 by analyst
vendors.funding_total_usd	https://www.crunchbase.com/organization/sarvam-ai Verified 2026-04-28 by analyst

Questions to ask before signing

Vendor-agnostic baseline. Send these to the vendor and require written answers before contract.

01. Provide your most recent SOC 2 Type II report (with bridge letter if applicable).
02. Describe your training-data provenance and customer opt-out mechanics in writing.
03. List all sub-processors and confirm notification policy for material additions.
04. Confirm BAA availability and signed-BAA process if we process PHI.
05. Describe rate-limiting, quota, and circuit-breaker controls protecting our usage.
06. Provide your model card or equivalent disclosure documenting intended use, limitations, and known failure modes.
07. Describe your prompt-injection defences and red-team posture against OWASP LLM Top 10 risks.
08. Confirm data residency options and which sub-regions our data may touch.
09. Provide incident-response SLAs, security-event notification timelines, and the most recent pen-test report summary.
10. Confirm output ownership terms and any indemnification or copyright-shield programs available.
11. Describe acquisition-risk safeguards and what happens to our data on a change of control.
12. List foundation-model dependencies and how upstream-model risk is mitigated.

Methodology + caveats

Composite scores use the default-balanced weight profile (25% data handling, 20% IP exposure, 15% jurisdiction, 15% security, 10% regulatory compliance, 8% transparency, 5% business stability, 2% dependency chain). All facts are sourced from the vendor's own public disclosures, public regulatory filings, or reputable secondary reporting — see the cited sources table above. This pack is decision-support material, not legal advice or audit evidence.