PhoenixAI announced that it raised $80 million in Series B funding led by Sky9 Capital, with participation from Atypical Ventures, Olive Technology Ventures, and existing investors. The financing will support continued development of the company’s AI-native database platform, expansion of go-to-market initiatives, and enhanced governance capabilities for regulated industries.
Formerly known as CelerData, PhoenixAI provides what it describes as an Agentic AI Database designed to deliver sub-second access to live enterprise data for autonomous AI agents operating at scale.
According to the company, the rapid adoption of AI agents is creating new demands on enterprise data infrastructure. Unlike human users, AI agents generate thousands of unpredictable, real-time queries that often require access to both historical and live data across multiple systems. Traditional databases typically rely on pre-modeled data structures built around anticipated questions, a model that becomes increasingly difficult to maintain as AI agents introduce novel workloads.
PhoenixAI said its platform addresses these challenges by enabling enterprises to process agent-driven workloads with high speed and governance. The company noted that organizations including AppLovin, Coinbase, Conductor, and Demandbase are already using its platform in production environments.
The company is led on the commercial side by President Rick Underwood, who previously served as CEO of Clumio and held senior go-to-market roles at Snowflake through its 2020 initial public offering.
PhoenixAI’s platform combines real-time and at-rest data within a single engine designed to deliver the performance and concurrency required for AI agents while maintaining the governance and deployment flexibility expected by enterprise customers.
Headquartered in Menlo Park, California, PhoenixAI plans to use the new funding to further establish itself as a foundational infrastructure provider for agentic AI applications.
KEY QUOTES:
“Today’s agentic landscape has moved quickly from planning and prototyping to full-on production for mission-critical work, serving customers, managing supply chains, and driving internal workflows. Agents now fire off thousands of unplanned, real-time queries, often swarming systems with questions that weren’t anticipated when the data stack was designed, demanding analysis across live data, historical records, and multiple systems at once, which strains existing data stacks. PhoenixAI closes that gap by giving enterprises a faster, more efficient way to serve those workloads at scale with the level of governance the C-suite expects.”
Rick Underwood, President, PhoenixAI
“PhoenixAI changed the equation: streaming updates from Kafka become queryable within seconds, analysts get sub-second responses on live normalized data, and our AI agents operate on the same real-time dataset. This level of performance at scale fundamentally changes what data teams can do.”
Xinyu Liu, Senior Staff Software Engineer, Coinbase
“PhoenixAI now serves that workload as the real-time analytical database on top of our data lakehouse. Production queries scanning hundreds of millions of rows return in under a second, and this is the architecture we need for the next generation of agentic workloads.”
Wei Zheng, Chief Product Officer, Conductor
“PhoenixAI gives us a fast, isolated warehouse for agent workloads directly on our Apache Iceberg tables, with the optimizer handling novel joins automatically.”
Ryan Nowacoski, Senior Engineering Manager, Data Platform, Demandbase
“The move to agentic AI is one of the largest infrastructure shifts we’ve seen, and the database is at the center of it. Enterprises can’t put agents into production until something can serve them live data at the speed and scale agents now demand. That’s the problem PhoenixAI was built to solve, and we’ve been impressed by the company’s early traction with demanding enterprise customers, we’re proud to lead this round and back the company defining the category.”
Ron Cao, Founder And Managing Partner, Sky9 Capital
