Sarvam AI is an India‑based AI company building “sovereign AI” models optimised for Indic languages and local contexts. Its latest systems—Bulbul V3 (voice assistant) and Sarvam Vision (OCR/document AI)—report outperforming general models like Gemini and ChatGPT on India‑specific tasks such as multi‑script OCR and vernacular interactions.

India’s push for “sovereign AI” isn’t just policy language—it’s showing up in working systems. Bengaluru‑based Sarvam AI has launched models built for India first: Bulbul V3, a natural‑sounding voice assistant for 11+ languages and Hinglish; and Sarvam Vision, a document intelligence/OCR system tuned for multi‑script Indian documents. Recent evaluations and media coverage suggest these models outperform general‑purpose systems in India‑specific tasks. Here’s what that really means and why it matters.

What “sovereign AI” means in practice

Sovereign AI describes capabilities developed and governed domestically—compute, models, data policy, and talent—so that critical services can run on infrastructure a country controls. In India, the IndiaAI Mission and partnerships with states signal a plan to produce foundation models, language infrastructure, and citizen‑scale applications across sectors such as governance, BFSI, healthcare, and education.

The models to know: Bulbul V3 and Sarvam Vision

Bulbul V3 (voice) focuses on smooth, human‑like speech and comprehension across Indian languages, accents, and code‑switching. It aims to reduce friction in citizen services and call‑centre workflows: think IVR replacement, eligibility triage, status queries, and form guidance—all in everyday language.

Sarvam Vision (OCR/document AI) tackles a stubborn problem: Indian documents that combine scripts (Devanagari, Latin, Bengali, etc.), low‑resolution scans, stamps, and hand‑filled fields. By being trained for these formats, it can improve extraction accuracy for KYC, compliance, and public‑service records, enabling automation where generic OCR often fails.

“Beat Gemini and ChatGPT”? A balanced view

Headlines claiming Sarvam “beat” global models compress a nuanced story. The core is this: in India‑specific tasks—particularly OCR for Indic scripts and local‑context language interactions—Sarvam’s specialised models have reported stronger results than general‑purpose systems. That doesn’t imply overall superiority in every task. Rather, it shows the value of domain‑optimised models for national and sector use cases.

Why this matters for organisations

• Citizen services & public sector: Multilingual assistants can deflect call volumes, answer status queries, and guide application flows in local languages; OCR accelerates digitisation of legacy records and KYC checks.

• BFSI & telecom: Faster onboarding with improved document capture and fraud checks; voicebots that actually understand regional accents.

• Healthcare & education: Vernacular intake and helpdesks; learning support for students in native languages.

Ecosystem signals: partnerships and open models

Sarvam has aligned with state governments to co‑build compute capacity, sovereign models, and skills programmes. Plans for a Sovereign AI Park in Chennai point to longer‑term infrastructure. The company has also stated intentions to open‑source models trained under IndiaAI Mission work, encouraging transparency and local adoption.

How it compares to global assistants

• Language & context: Global models like Gemini or ChatGPT excel broadly, but can struggle with Hinglish code‑switching, regional idioms, or rare scripts. A locally tuned model can lead on these edges.

• Document intelligence: Generic OCR often underperforms on mixed‑script scans and low‑quality images common in Indian workflows. Sarvam Vision’s training focus gives it an advantage for these inputs.

• Ecosystem fit: Sovereign deployments can meet data‑residency and public‑procurement expectations, while still interoperating with global platforms where appropriate.

Practical adoption checklist

1. Start with a pilot in one high‑volume flow (e.g., KYC OCR or multilingual contact‑centre deflection).

2. Measure accuracy & CSAT vs your current stack; track containment, handle time, and downstream rework.

3. Design for code‑switching and regional scripts; include real samples from your queues.

4. Plan governance early—red‑team prompts, escalation paths, and audit logs.

5. Integrate with existing tools (CRMs, ticketing, M365/Google) so AI sits inside work—not beside it.

Risks and considerations

• Benchmark generalisation: Gains on India‑specific tests may not translate to other domains; validate on your real data.

• Model drift & updates: Keep test suites for languages/scripts you serve; retrain or fine‑tune as needs shift.

• Compliance: Confirm data‑handling with your legal team, especially for PII in documents and voice recordings.

Bottom line

Sarvam AI isn’t trying to be the best at everything. It’s aiming to be the best at India’s hardest AI problems—multilingual voice and messy, multi‑script documents. For organisations serving Indian users, that specialisation can be the difference between a flashy demo and a live, reliable service.

Frequently Asked Questions

Is Sarvam AI truly better than Gemini or ChatGPT?
It reports higher performance on India‑specific tasks like Indic OCR and local language interactions. That’s not the same as across‑the‑board superiority. Evaluate on your workflows.

What’s Bulbul V3 used for?
A voice assistant for natural conversations across Indian languages and Hinglish—ideal for public‑service helplines, customer care, and guided processes.

What is Sarvam Vision?
A document AI/OCR system built for Indian scripts and real‑world document noise (stamps, low‑res scans), used in KYC and records digitisation.

Is this part of India’s sovereign AI plan?
Yes—Sarvam aligns with the IndiaAI Mission and state‑level partnerships to develop domestic compute, models, and skills.

Can enterprises adopt it today?
Start with a scoped pilot; integrate with your CRM/ITSM and evaluate accuracy, cost, and governance vs global models.

Summary & Next Steps

If you operate in India, test India‑optimised models where they matter most: multilingual support and document processing. For help designing a pilot, governance, and integration plan, talk to Generation Digital.

Contact Generation Digital