Indian artificial intelligence startup Sarvam AI has introduced Prism, a decision-focused AI model designed to help enterprises automate tasks that require fast and consistent choices across predefined options.
Built on the company’s Sarvam 105B model, Prism is designed for what Sarvam describes as System One decision-making, referring to fast and intuitive judgements. Unlike conventional conversational AI models that generate text, Prism evaluates a given situation and returns probabilities for a set of possible answers.
The model is intended for enterprise workflows involving decisions such as routing, classification, moderation, triage, security checks and evaluating the performance of AI agents.
According to Sarvam, Prism achieved an average score of 71.8 across three decision benchmarks, with a median response time of 44 milliseconds when running on an NVIDIA B200 GPU. The company said this was the higSarvam AI Introduces Prism Model for Millisecond-Level Enterprise Decisionshest-scoring model among the 25 decision models it tested that delivered responses in under 100 milliseconds.
Sarvam said the model was designed to provide calibrated confidence, allowing businesses to assess how reliable its predictions are before using them in automated workflows. The company reported that Prism’s errors remained within its stated confidence bounds across the English and Indian-language tests it conducted.
The company also tested whether the order in which answer options were presented affected the model’s decisions. In a test involving 2,000 questions, Sarvam said Prism returned the same answers when the options were rearranged.
Prism is being released with open weights under the Apache 2.0 licence. This allows enterprises to access the model, fine-tune it using their own data and deploy it on their own infrastructure. Businesses can also use Sarvam’s infrastructure and API for deployment.
The model accepts a given context, a question and a list of possible choices, then returns a probability for each option. This format is intended to reduce the need for businesses to use larger conversational models for simple classification and decision tasks.
Sarvam said it had reduced the model’s median response time from an initial 664 milliseconds to 44 milliseconds through optimisation techniques, including FP8 precision and changes to GPU execution.
The release adds a specialised decision model to Sarvam’s AI portfolio, with a focus on speed, confidence calibration and enterprise deployment. The benchmark figures and performance comparisons cited in the announcement are based on Sarvam’s own testing.
