LLUMO AI, an artificial intelligence optimisation company has raised $1 million in seed round funding led by Rahul Agrawalla, General Partner at Sense AI. The funding round also saw participation from India Quotient, Aum Ventures, Venture Catalysts, IIM Indore Alumni Angel Fund, and a host of US-based angel investors.
The seed funding will enable Llumo AI to enhance its technology, expand its team of AI experts, and accelerate its go-to-market strategy.
Shivam Gupta, Founder of LLUMO AI, said, “We founded LLUMO AI with a clear mission: to democratise access to generative AI and help businesses unlock its true potential. Our platform not only addresses the critical challenges of cost and performance but also empowers our customers to make data-driven decisions that accelerate growth and transform customer experiences. With this funding, we are one step closer to realising our vision of making generative AI accessible, affordable, and impactful for businesses worldwide.”
Founded by Shivam Gupta, an IIT Roorkee alumnus, and Akshat Anand, an IIT Kanpur alumnus, Llumo AI has quickly established itself as a frontrunner in the enterprise SaaS space.
The company offers a platform that significantly reduces LLM costs and time-to-market, making AI more accessible and profitable for businesses.
The platform helps businesses refine their AI performance at 10 times the speed, enabling them to consistently deliver high-quality, AI-driven customer experiences. This results in enhanced revenue streams and greater profitability for Llumo AI’s clients.
Llumo AI’s platform is designed to deliver measurable, production-ready benefits to AI companies by cutting LLM costs by 80%, reducing time-to-market by 90%, and minimizing prompt engineering hours—valued at $150,000 annually per person.
Its solutions are two proprietary tiny LLMs that are trained on millions of data points. The first model compresses prompts, significantly reducing costs while maintaining output quality while the other is Eval-LM (Evaluation Language Model), which assesses LLM-generated output without requiring ground truth data. By leveraging these models, businesses can optimize their LLM implementations, achieve faster iterations, and drive growth without straining their budgets.
