PhoenixAI Raises $80M Series B Funding Led by Sky9 Capital

The round also saw participation from Atypical Ventures and Olive Technology Ventures, and previous investors.

The company plans to use the funding to speed up development of its AI-native database, expand its go-to-market efforts, and strengthen governance features for regulated industries.

Enterprise demand for infrastructure built specifically for AI agents is growing quickly. Unlike humans, AI agents ask real time, unpredictable questions that traditional analytics systems were never designed to handle.

Because of this, answering agent queries requires combining live and historical data instantly in ways that traditional databases cannot support. Conventional systems rely on pre modeling data for expected human questions, but this approach fails when agents begin generating new types of queries at scale.

"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," said Rick Underwood, President of 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, "said Xinyu Liu, Senior Staff Software Engineer, Coinbase.

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"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, "said 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, "said 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," said Ron Cao, Founder and Managing Partner, Sky9 Capital.

About PhoenixAI

Founded in 2022, by James Li, Andy Ye, and Alvin Zhao, PhoenixAI is an agentic AI database built to give autonomous AI agents fast, sub-second access to live enterprise data at scale. The platform combines real time and historical data into a single AI native system, enabling high speed, high concurrency workloads while still providing the governance, and flexibility enterprises need to deploy AI in production safely.

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