PuppyGraph has raised $10 million in Series A funding led by Particle Future and defy.vc, bringing its total funding to $15 million as it expands engineering and enterprise sales efforts.
The financing follows the general availability of PuppyGraph 1.0, a graph query engine designed to help AI agents investigate relationships across enterprise data without moving that information into a separate graph database.
The company says annual recurring revenue increased sevenfold over the past year. Customers include AMD, Palo Alto Networks, Datadog, Netskope, Coinbase, Trend Micro and Baker Hughes.
PuppyGraph addresses the complexity of answering questions that span thousands of tables and multiple business systems. Its software enables data to be queried as a unified graph while remaining in supported platforms such as Apache Iceberg, Databricks, Snowflake, ClickHouse, DuckDB, PostgreSQL, Microsoft Fabric and Apache Hudi.
An AI-assisted schema builder generates graph structures from existing tables for teams to review and approve. Users can then investigate the connected data through PuppyGraph’s built-in agent or compatible AI tools using Model Context Protocol.
The engine also rejects references to undefined relationships and returns feedback that agents can use to correct queries.
PuppyGraph says its distributed architecture supports petabyte-scale workloads and subsecond graph queries. In one reported test, it completed a 10-hop query across billions of relationships in 2.26 seconds using a four-machine cluster.
Security features include row-level permissions throughout graph traversals, single sign-on, role-based controls and user impersonation. Short-lived credentials support connections to platforms including Snowflake and Databricks, while administrative audit logs record management activity.
The funding will support expansion of a product designed to give enterprise AI applications access to connected data without maintaining another copy or associated migration pipelines.
KEY QUOTES
“The insights of enterprise data will soon be generated by agents. Users will expect to ask questions that cross departments and systems, beyond the workflows someone built into a dashboard. That requires agents to navigate thousands of tables, follow the right relationships, and retrieve evidence in subseconds.”
Weimo Liu, Co-Founder And CEO Of PuppyGraph
“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. By removing the ETL bottleneck, we eliminated pipeline maintenance entirely while delivering lightning-fast graph analytics to our end users in real time.”
Ran Xia, Engineering Director At Palo Alto Networks
“Big data is still an unsolved problem in the world of AI. You can ask Claude to write a query, but executing it across billions of relationships takes serious engineering. PuppyGraph was built from day one for performance at scale, giving agents a query engine that works directly on the data enterprises already have.”
Amy Yin, Partner At defy.vc
