Arize AI, a company focusing on AI observability and LLM evaluation, has announced a remarkable $70 million Series C funding round. The company claims this investment marks the largest ever in the realm of AI observability.
The investment was spearheaded by Adams Street Partners, with contributions from other investors, including M12 (Microsoft’s venture fund), Sinewave Ventures, OMERS Ventures, Datadog, PagerDuty, Industry Ventures, and Archerman Capital.
Arize AI’s recent research initiative, OpenEvals, has revealed that LLMs often struggle to accurately evaluate synthetic datasets—data generated by other AI models—compared to non-synthetic data. Furthermore, the results from the research state that LLMs are still unpredictable, difficult to troubleshoot, and prone to failure.
Hence, with the company’s offering, organisations can course-correct AI systems before they do any damage in a real-world setting.
Jason Lopatecki, CEO and co-founder of Arize AI, emphasised the importance of reliability in AI systems: “Building AI is easy. Making it work in the real world is the hard part. Enterprises can’t afford to deploy unreliable AI.” He stressed that engineering teams require robust infrastructure to test and troubleshoot their models effectively before they impact customers.
The company mentions that its platform has helped enterprises like Uber, Duolingo, TripAdvisor, and more. And, they also highlight that their open source offering, Arize Phoenix, is the most widely adopted AI observability and evaluation library for development.
In a statement, Arize says that the partnership with Microsoft is also set to deepen, with M12’s investment reinforcing their collaboration, which will result in better integrations with the Azure AI platform.
Aparna Dhinakaran, the chief product officer and co-founder of Arize, shared a bit about their future vision, “As AI research and real-world applications accelerate, Arize will continue to pioneer new tools, like our recent first-to-market launch of audio evaluation for voice assistants, to help engineers working on these systems better evaluation, debug, and improve what they build.”
With more companies focusing on solving issues related to AI, such as the interesting concept of using AI to fix AI, the experience for businesses, clients, and customers should improve.
