Sarvam’s Prism Enters India’s Growing AI Decision Model Race

by · Inc42

SUMMARY

  • Sarvam AI has launched Prism, an open-weight decision model built on its Sarvam 105B model for fast, structured decisions across enterprise workflows.
  • The launch puts Sarvam alongside Indian builders such as ConvAI Innovations and PurpleTalk’s Sthānika, as well as global players developing models designed to make decisions rather than generate text.
  • Sarvam claims Prism achieves an average score of 71.8 across three evaluation indices, with a median response time of 44 milliseconds on an Nvidia B200 GPU.
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AI models are increasingly being built to do more than answer questions. Some are now designed to make specific decisions, such as whether a customer query should go to a sales or support team, whether a document needs further review, or whether an AI agent has completed a task correctly.

In India, Kerala-based ConvAI Innovations was one of the first to explore this category with Laya, an open-weight decision model intended to run locally on devices such as laptops and phones. Hyderabad-based PurpleTalk’s research lab, Sthānika AI, has also developed Sieve, a family of decision models that returns probabilities for a fixed set of possible answers. 

Both startups are betting that specialised models can make these decisions faster and more efficiently than general-purpose AI systems. 

The space has now attracted Sarvam AI, one of India’s prominent homegrown AI model developers. The company introduced Prism, a decision model built on its 105B-parameter foundation model. Sarvam says Prism combines high accuracy with low response times, while allowing enterprises to customise and run it on their own infrastructure.

The launch raises a bigger question for India’s AI ecosystem, especially given the implications on cost, reliability and speed: Are decision models the opportunity for Indian AI startups to compete with global players, rather than trying to match them across every general-purpose AI capability?

What Are Decision Models, And Why Now?

Most large language models (LLMs) are designed to generate text. Even when asked to make a simple choice, they typically produce a response that another software system must interpret. This can add processing time, increase costs and introduce inconsistencies.

Decision models approach the problem differently. They receive information, a question and a defined set of possible answers, then return a score or probability for each option. Instead of generating a paragraph explaining which department should handle a customer complaint, for instance, the model can assign probabilities to sales, support and billing.