Sarvam AI: India-First AI for Voice, OCR, and Agents

Why is India-First Language AI Uniquely Hard?

India has hundreds of languages and dialects, many different accents, and multiple ways of writing or speaking the same language. Unlike English and other widely studied languages, Indian languages often lack large and clean digital datasets. That makes it harder for global AI models to correctly understand speech, text, or documents in these languages.

Additionally, content in India often mixes languages (such as Hindi‑English Hinglish), regional expressions, and handwritten or poorly scanned documents. Because of these challenges, developing India‑first AI for voice, OCR, and intelligent agents requires more rigorous training, specialized models, and testing.

What Did Sarvam AI Launch in 2026?

To address the challenges of Indian languages, Sarvam AI introduced updates in 2026. Let’s look at the key launches.

1. Bulbul V3

Bulbul V3 is the newest text-to-speech model. It can speak in more than 35 voices across 11 Indian languages. It’s designed to handle real-life situations, like regional accents, mixed languages like Hinglish, numbers, abbreviations, and even phone-quality audio. This makes the speech sound clearer and more natural, even in situations where global models struggle.

2. Sarvam Arya

Sarvam Arya is a system that helps developers build AI workflows by connecting smaller AI components. Instead of relying on one model to do everything, Arya splits tasks like reasoning, data extraction, and task control into separate blocks. This makes it easier to handle complex enterprise tasks, such as processing financial reports or managing large document pipelines.

3. Sarvam Vision

Sarvam Vision can read documents in Indian languages. It supports scanned pages, tables, forms, and mixed scripts. This makes it handy for digitizing official forms, regional records, or any document where other systems struggle with Indic scripts.

4. Sarvam Dub

Sarvam Dub is a tool that can dub what someone says into other Indian languages while preserving their voice. It doesn’t just translate words; it preserves the speaker’s tone and pace, so videos, lectures, and speeches sound natural. People are already using it in schools, on TV, and for public talks, and it works really well across different languages.

5. Sarvam Samvaad

Sarvam Samvaad is a platform for creating conversational AI agents. It works across most Indian languages and enables businesses to build voice or chat assistants for phones, WhatsApp, websites, and apps. You can connect it to CRMs, phone systems, and databases, and it also shows how the assistants are performing.

Product Summary

Key Products of Sarvam AI

Here’s a broader view of the key products and platforms they offer, along with what each one does.

• Foundational Language Models

Sarvam offers Indian-language models, such as Sarvam-105B and Sarvam-30B. These models are specialized, high-performance LLMs designed for Indian languages. They process conversational, reasoning, and voice-based tasks.

• Shuka: Audio Understanding and Encoding

Shuka is a voice and audio foundation model that processes Indian-language speech input and converts it into text or other meaningful representations. It combines audio encoders with large language model backbones to support multimodal applications, including voice assistants, speech understanding, and voice-first interfaces.

• Sarvam Models API Suite

Sarvam offers a suite of API accessible AI models for common tasks across speech, language, and document processing. These include speech-to-text, text translation, speech synthesis, and structured document parsing. Developers can call these APIs to embed Sarvam’s Indian language capabilities into their own products or services.

• Agents & Conversational Platform

Sarvam provides a platform for deploying intelligent AI agents that understand and respond in Indian languages. These agents can be programmed to handle voice and text interactions, perform actions, integrate with business systems, and capture conversation insights for analytics and automation.

• A1: GenAI Workbench for Professional Workflows

Sarvam’s platform includes A1, a Generative AI workbench tailored for professional users, especially legal workflows. It helps with tasks like regulatory chat, document drafting, redaction, and structured data extraction from text and files, making complex work faster and more accurate.

Sarvam AI vs ChatGPT, Gemini, and LLaMA: Key Differences

Beyond product knowledge, it’s also useful to see how Sarvam compares with global models. Here’s a look at the key differences and strengths:

1. Sarvam AI vs OpenAI

OpenAI’s GPT models are highly capable and can handle a wide range of tasks, from coding to chat to general problem-solving. They work in many languages and across different types of work. Sarvam AI is a bit different; it’s focused on Indian languages.

Its models can convert text to speech, speech to text, and read scanned documents in Indic scripts. That makes Sarvam a better fit for India-specific language tasks, though it doesn’t replace GPT for everything else.

2. Sarvam AI vs Google Gemini

Google's Gemini models can handle a wide range of inputs, primarily text, and cover a broad range of tasks across many languages. Sarvam AI keeps things more focused. It supports language processing and document understanding in Indian contexts, such as reading forms with mixed scripts using Sarvam Vision.

Gemini aims to cover global use cases, while Sarvam zeroes in on Indian languages and practical tasks.

3. Sarvam AI vs Meta LLaMA

Meta’s LLaMA models are open-source and trained on many languages for research and general AI tasks. Sarvam’s models, such as Sarvam-1 and Sarvam-M, are designed for Indian languages.

They handle different scripts and regional variations well. Because of its focus on OCR and speech-to-text in Indian languages, Sarvam operates slightly differently from LLaMA, which is designed for more general use.

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What Are the Most Practical Sarvam AI Use Cases Today?

From the above differences, it is clear that Sarvam AI is specifically designed to handle Indian languages, regional scripts, and local data challenges. Now, let’s look at the applications in detail:

• Customer Support

Sarvam AI’s conversational agents help businesses handle customer support across multiple Indian languages, including Hindi, Tamil, and Telugu. They can answer common questions, book appointments, and give order updates through phone, WhatsApp, or web chat. This means companies can manage multiple support requests simultaneously without additional staff, and customers receive faster responses.

• Document Processing

Sarvam Vision and related tools are applied to process scanned documents, forms, and mixed‑script records in Indian contexts. These models can extract structured data from tables, handwritten sections, and complex layouts with greater accuracy, enabling workflows such as digitizing government records, processing invoices, and organizing large paper document repositories. This capability supports enterprise and institutional digitization efforts.

• Education

Edtech platforms can use Sarvam’s speech and language tools to teach students in Marathi, Gujarati, Telugu, and more. It explains things in simple, everyday language, so students who are more comfortable in their own language can follow along and keep up in class.

• Public Services

Government and social programs use Sarvam’s voice and agent technologies to reach citizens in their own languages at scale. For example, AI voice outreach has been used to contact millions of people across states with information about public schemes, answer citizen questions, and assist with services in both rural and urban regions. This makes essential services more accessible to non‑English speakers.

• Other Practical Applications

Sarvam models are also used across healthcare, legal services, and e‑commerce. In healthcare, voice AI can enable patients to book appointments or access information in their preferred language.

In online shopping, it can help answer questions and support orders in multiple languages. These are good examples of how AI is being used in everyday tasks across different sectors.

How to Evaluate Sarvam AI for Your Project?

Before applying Sarvam AI to your application, evaluate its performance and fit for your needs. Here is how you can do it effectively.

#1. Voice Evaluation Checklist

When you evaluate voice capabilities, focus on both technical accuracy and user experience across the speech pipeline, from input recognition to output quality.

Measure how well the system transcribes speech (ASR) using word error rate, how natural and intelligible the text‑to‑speech sounds, and how quickly responses occur. 

Effective voice evaluation also tests understanding of intent and handling of interruptions, accents, and multiple turns in a conversation. You should include stress tests for background noise and real‑world speech conditions to ensure reliable performance.

#2. OCR Evaluation Checklist

For optical character recognition, assess how accurately scanned documents are converted into structured text across the languages and scripts you care about. This includes measuring character and word-level accuracy, the ability to parse complex layouts such as tables and forms, and robustness to variations in scan quality and handwriting.

Testing should cover real-world use cases with diverse samples to assess how consistent and reliable the model’s output is for your specific document types.

#3. Agent Evaluation Checklist

For conversational agents, evaluate task success and flow handling rather than just single replies. Check whether the agent correctly understands user intent, completes tasks without human intervention, and gracefully escalates or hands off when needed.

Evaluate the dialogue performance over multiple turns, how well it retains context, and how it handles edge cases or unexpected inputs. Measure latency and user satisfaction metrics to ensure a smooth, responsive experience.

How Sarvam AI Fits into India’s Sovereign AI Push

Now that you’ve explored Sarvam AI’s recent launches, products, and applications, let’s see how it aligns with India’s push for sovereign AI:

• Foundational AI Model Development

Sarvam AI is developing large language models as part of India’s push to build a homegrown AI ecosystem. Under the IndiaAI Mission, Sarvam is developing models for text generation, translation, and chat, all designed for local needs. The goal is to rely less on foreign AI and build stronger AI tools grounded in Indian data and contexts.

• Advanced Speech and Vision Systems

In addition to language models, Sarvam has developed systems for speech recognition, text‑to‑speech, and document understanding across diverse content types.

Such systems can be used within national platforms to deliver services in multiple languages with the nuance needed for high‑volume public interaction and multilingual communication.

• Strategic State Partnerships

Sarvam is partnering with state governments in Odisha and Tamil Nadu to establish local AI compute centers and research hubs. These projects bring together the equipment, infrastructure, and support needed to develop and use advanced AI tools in the region.

• Infrastructure for AI Research and Governance

Sarvam is not just building AI models and tools. They’re also establishing the infrastructure that enables researchers, startups, and public institutions to share computing resources. This makes it easier to run larger experiments, train models, and deploy AI locally, supporting India’s push for homegrown AI without relying solely on external providers.

What Are the Limitations and Risks of Sarvam AI?

Although Sarvam AI has made notable progress with India‑focused models and tools, there are also some limitations and risks to be aware of. Let’s look at the key considerations before adopting it for your project.

Narrow Scope Compared to General AI Models

Sarvam AI’s models are optimized for specific Indian-language tasks, including text-to-speech, OCR, and regional speech recognition. They are not designed as general‑purpose AI systems capable of handling tasks such as broad reasoning, coding assistance, or long-form conversational interactions, which larger global models are built for.

Data and Context Challenges

Like any AI, Sarvam AI can get confused when the input isn’t clear or is tricky. Sometimes it may misinterpret the context or provide an incorrect answer, especially when questions are complex. This can be a problem when getting things exactly right matters.

Resource and Infrastructure Limitations

Developing and scaling AI models requires significant computational resources. Compared to global giants, Sarvam AI currently operates with relatively limited infrastructure, which can restrict the scale of model training, the size of models it can support, and performance on very large workloads.

Ethical and Misuse Risks

Advanced AI can be risky, too. For example, voice cloning or synthetic speech could be misused to impersonate people or spread false information. Ensuring user data is secure and preventing harmful uses of AI content are areas the industry is still working on.

Rules and Regulation Uncertainty

The rules and regulations for AI are still evolving in many regions, including India. Ensuring compliance with data privacy, fair use, and ethical standards remains a challenge for developers and organizations adopting AI systems.

What to Learn Next if You Want to Build with India-First AI?

To build with India-first AI like Sarvam, you need a mix of foundational AI knowledge, practical skills, and hands-on experience with Indian language datasets and tools. Here’s a roadmap to get started and gradually level up your expertise.

• Strengthen Core AI and Machine Learning Knowledge

Begin with the basics of AI and machine learning. Focus on neural networks, natural language processing, and audio or text processing. Understanding these concepts will help you work effectively with pre-built India-first AI models and integrate them into real-world applications.

• Gain Hands-On Skills with Speech and Document AI

Models such as Bulbul V3 and Sarvam Vision support speech and text. You can start by using text-to-speech, speech recognition, or document digitization. Playing with small projects or open-source tools is a good way to see how these systems handle audio and text in practice.

• Build Practical Experience Through Courses

Structured learning can accelerate your journey. Simplilearn offers courses in AI, machine learning, NLP, and data engineering that include hands-on labs and real-world projects. These courses provide the knowledge and practice needed to work effectively with India-first AI tools and APIs.

• Learn to Deploy AI in Real-World Workflows

Once you’re comfortable with the technical skills, explore how AI can be integrated into workflows like customer support, document automation, or conversational agents.

Understanding deployment, API usage, and system integration will allow you to leverage India-first AI models for practical use cases.

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Key Takeaways

• Sarvam AI builds AI tools and models that work really well with Indian languages
• Their products, such as Bulbul V3, Sarvam Vision, and Samvaad, solve real problems across customer support, education, government services, and business workflows
• By focusing on India-first AI, Sarvam AI helps build local infrastructure and works with state initiatives, cutting down reliance on foreign systems
• Even though these tools are powerful for India-specific tasks, it’s still important to understand their limitations, assess the infrastructure requirements, and consider ethical implications before using them

Additional Resources

• Types of Generative AI Models
• Artificial Intelligence Tutorial for Beginners
• How to Become an AI Engineer: Skills, Roles, and Career Guide
• AI vs Human Intelligence: Key Differences and Insights
• Top AI Jobs 2026: Expert Guide to Build Your Career
• Essential AI Certifications for 2026
• Advantages and Disadvantages of AI
• What is Agentic AI?
• Future of AI

FAQs

1. What is Sarvam AI used for?

Sarvam AI is used to build India‑focused generative AI tools for tasks such as multilingual voice assistants, text‑to‑speech, speech recognition, document OCR, and conversational enterprise agents.

2. What does Sarvam AI do?

Sarvam AI develops large language models and AI systems tailored to Indian-language contexts, enabling voice‑first interactions, language translation, document understanding, and customizable chat and business workflows.

3. Who is the CEO of Sarvam AI?

The CEO of Sarvam AI is Pratyush Kumar, and the founder of Sarvam AI is Dr. Vivek Raghavan, along with Dr. Pratyush Kumar.

4. What is the salary of an AI engineer in Sarvam?

There is no publicly verified salary data for AI engineers at Sarvam AI; compensation varies by experience, role, and location, and isn’t officially disclosed.

5. Who invested in Sarvam AI?

Sarvam AI raised about $41 million in a Series A funding round led by Lightspeed Venture Partners with participation from Peak XV Partners and Khosla Ventures.

6. What is Bulbul V3?

Bulbul V3 from Sarvam AI is a text-to-speech model that supports multiple Indian languages. It’s good at mixing languages naturally and sounding more human.

7. What is Sarvam Arya?

Sarvam Arya is a workflow system from Sarvam AI that connects AI components to handle complex tasks such as reasoning, data extraction, and control logic in enterprise applications.

8. What is Sarvam Vision used for?

Sarvam Vision is used for document intelligence and OCR, converting scanned pages, tables, and forms into structured text and understanding complex layouts across varied Indian scripts.

9. Is Sarvam AI better than ChatGPT/Gemini for India tasks?

Sarvam AI can outperform general models like ChatGPT and Google Gemini on India‑centric tasks such as OCR and Indic-language speech generation because it is optimized for local languages and mixed-scripting. However, it is not a full replacement for broader global AI use cases.

10. Is Sarvam AI available on Azure?

Sarvam AI collaborates with Microsoft to make its Indic large language model for voice available on Azure, enabling developers to build and scale voice‑based generative AI applications on that platform.