AI infra startup Future AGI raises $1.6 million in round co-led by Powerhouse Ventures, Snow Leopard Ventures

Angellist Quant Fund, Swadharma Source Ventures, and Saka Ventures, among others, participated in the pre-seed funding round. The funds will be used for product development, expanding the engineering and growth teams and strengthening the tech stack.

Agencies
Artificial intelligence (AI) infrastructure company Future AGI has raised $1.6 million in a pre-seed funding round co-led by Powerhouse Ventures and Snow Leopard Ventures, with participation from Angellist Quant Fund, Swadharma Source Ventures, and Saka Ventures, among others.

The funds will primarily be used for product development and expanding its engineering and growth teams while strengthening its proprietary technology stack. They will also support the growth of the company’s workforce and customer base.

“AI is becoming the new software, but its widespread adoption faces a critical challenge - reliability and accuracy at scale,” said Nikhil Pareek, CEO of Future AGI. “Today’s AI systems are probabilistic and error-prone, with improvement cycles taking six to eight months. We're building the foundational layer that ensures AI systems are trustworthy and reliable in production.”


“The AI landscape is evolving rapidly, and one of the biggest challenges enterprises face today is ensuring the accuracy and reliability of their AI applications,” said Sri Peddu, general partner at Powerhouse Ventures. “Future AGI’s innovative approach to solving this critical problem through their comprehensive AI lifecycle management platform positions them uniquely in the market.”

Founded in 2024, Future AGI is headquartered in the US with a research and development (R&D) centre in India.

The company offers comprehensive tooling, feedback, and intelligent layers, including its own fine-tuned large language models (LLMs). Its proprietary technology features advanced evaluation systems for text and images, auto-tuning prompt optimisers, and auto-annotation tools that can reduce AI product development time by up to 95%.
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