PuppyGraph, a graph query engine startup for enterprise artificial intelligence (AI), has raised $10 million in a Series A round led by Particle Future, with participation from defy.vc.
“The insights of enterprise data will soon be generated by agents,” Weimo Liu, Co-founder and CEO of PuppyGraph, said in a statement. He added that agents would need to navigate thousands of tables, follow relationships, and retrieve evidence across departments and systems.
The funding takes the US-based company’s total capital raised to $15 million and will support engineering and enterprise go-to-market expansion, the company said.
The announcement follows the general availability of PuppyGraph 1.0, which allows AI agents to query data across up to 3,000 enterprise tables as a unified graph without transferring the underlying data into a separate graph database, the company said.
According to PuppyGraph, AI agent projects that initially cover 30–50 tables can become difficult to scale when production workloads require connections across thousands of tables and multiple systems. The platform is designed to help enterprises run queries across databases, data warehouses, and data lakes.
PuppyGraph 1.0 includes an AI-assisted schema builder that generates graph schemas from existing enterprise tables for data teams to review and approve. Agents can then explore connected data using natural language through the platform's built-in AI agent or compatible tools connected through the Model Context Protocol (MCP).
The engine also rejects undefined relationship references and returns feedback that agents can use to correct their queries. It aims to prevent agents from making unsupported assumptions about how enterprise data is connected, the company said.
PuppyGraph said its distributed query engine supports petabyte-scale workloads and subsecond graph queries for supported workloads. In a separate benchmark, it completed a 10-hop query across billions of edges in 2.26 seconds using a four-machine cluster.
The platform supports data sources including Apache Iceberg, Databricks, Snowflake, ClickHouse, PostgreSQL, and Microsoft Fabric. It also enforces configured row-level permissions during graph traversals and offers role-based access controls and single sign-on.
The company counts AMD, Palo Alto Networks, Datadog, Netskope, Coinbase, Trend Micro, Baker Hughes, and Sola Security among its customers. Its annual recurring revenue grew sevenfold over the past year, although it did not disclose the revenue figure.
“For a user-facing application, query latency and data freshness are non-negotiable. PuppyGraph allows us to execute multi-hop graph traversals directly on our existing data platform without moving or duplicating a single row,” said Ran Xia, Engineering Director at Palo Alto Networks.
The company said that developers can access PuppyGraph through a free Docker deployment, without submitting a payment method or registration form.
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