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Bhavish Aggarwal's Krutrim launches AI assistant Kruti for daily tasks
Krutrim, the artificial intelligence startup co-founded by Ola founder Bhavish Aggarwal, on Thursday launched Kruti, an AI assistant it says represents a leap beyond conventional chatbots.
Unlike earlier tools focused on passive text replies, Kruti is positioned as an 'agentic AI'—designed to proactively carry out tasks such as cab booking, food ordering, bill payments, image generation and research. The launch builds on Krutrim's earlier public beta and introduces a redesigned interface with a focus on advanced reasoning and personalisation.
'Kruti is the first real step towards the future of AI where technology doesn't just talk back, but actually helps you get things done,' said Bhavish Aggarwal, founder of Krutrim. 'We have built Kruti to work the way Indians live: multilingual, mobile-first and intuitive.'
The platform also includes features such as read-aloud responses and free access to premium AI capabilities like research and image creation. The company said Kruti aims to make advanced AI more accessible to Indian users by combining broad functionality with local context.
Kruti understands both voice and text input, remembers past interactions and tailors responses in tone, length and language, including 13 Indian languages. Whether it's 'book me a cab to the airport' or 'order my usual lunch', Kruti understands intent and executes the task without micromanagement.
Krutrim said Kruti is powered by its latest large language model, Krutrim V2, alongside cutting-edge open-source systems, enabling scalable and cost-efficient performance tailored for Indian users.
Personalisation is at the heart of Kruti's design. It learns user preferences, adapts to usage over time and integrates with various apps and services to deliver context-aware assistance with minimal effort.
Kruti also includes a fully embeddable software development kit (SDK), allowing developers to integrate large language model orchestration, memory handling and tool execution with minimal code. By offering features such as research tools, image generation and task-based agents at no cost, the company is positioning Kruti as a more accessible and affordable alternative in the AI space.
Built with human-centred design, Kruti aims to eliminate app-switch fatigue and formats responses for clarity using summaries, tables and stories. It reflects how Indians use and relate to technology in everyday life.
Krutrim is part of a growing ecosystem of Indian companies building AI solutions, often with a focus on local languages and data. In the Indian context, it competes with global AI giants such as OpenAI, Anthropic and Google, as well as local players such as Sarvam AI and CoRover.ai.
Earlier this year, Bhavish Aggarwal announced an investment of ₹2,000 crore in Krutrim, with a commitment to invest an additional ₹10,000 crore by next year. The company also launched the Krutrim AI Lab, published several technical reports and released some of its work to the open-source community. The move followed DeepSeek's decision to open-source its own generative AI (GenAI) model.
BharatGPT Mini enters the fray
On Thursday, CoRover, a conversational AI firm, also launched BharatGPT Mini, a compact small language model (SLM) designed for use in low-compute and infrastructure-constrained settings. Trained on the company's proprietary conversational dataset, the 534-million parameter model supports text-based tasks in 14 Indian languages, aiming to expand AI access across underserved regions.
The model is optimised for edge and server implementations, enabling seamless AI performance on low-end devices. This strategic development addresses the growing demand in India for cost-effective, fast and privacy-centric AI solutions that operate without reliance on high-end cloud infrastructure.
CoRover said India's need for SLMs is accelerating, driven by demand for faster, localised and cost-efficient AI tools that maintain data privacy. While large language models (LLMs) remain essential for complex, multi-domain tasks, SLMs are increasingly preferred for their domain specificity, speed, offline functionality and ease of integration on constrained devices.
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