PuppyGraph, the graph query engine for enterprise AI agents, today announced a $10 million Series A led by Particle Future and defy.vc, bringing total funding to $15 million. The round comes after the general availability of PuppyGraph 1.0 and will fund engineering and enterprise go-to-market expansion.

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PuppyGraph has won adoption from some of the world’s leading enterprises, including AMD, Palo Alto Networks, Datadog, Netskope, Coinbase, Trend Micro, Baker Hughes, Sola Security, HighStreet Insurance Partners, Prevalent AI, and more. Over the past year, annual recurring revenue grew 7x.

Enterprises want AI agents that can answer questions across their business. A proof of concept may cover 30 to 50 selected tables. In production, those questions can require connections across 3,000 tables and multiple systems. “If this account is compromised, which critical systems could an attacker reach?” Answering requires following relationships across identities, permissions, and infrastructure.

As the number of tables grows, schema descriptions consume more of an agent’s context window and the possible join paths multiply. Deep relationship queries can become too slow for interactive agent investigation, or time out entirely. And moving the data into a separate graph database trades one set of problems for another: loading delays, pipeline maintenance, and performance and scalability limits at enterprise data volumes.

PuppyGraph 1.0 enables an AI-assisted graph schema builder that reduces manual modeling by automatically generating a graph schema from existing tables for teams to review and approve. Users can investigate connected enterprise data in plain English through PuppyGraph’s built-in AI agent or compatible AI tools they already use, connected through MCP. To help agents avoid errors from guessing how data connects, the query engine rejects undefined relationship references and returns feedback agents can use to correct their queries.

“The insights of enterprise data will soon be generated by agents,” said Weimo Liu, cofounder and CEO of PuppyGraph. “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.”

Introducing PuppyGraph 1.0

PuppyGraph brings graph compute directly to Apache Iceberg, Databricks, Snowflake, ClickHouse, DuckDB, PostgreSQL, Microsoft Fabric, Apache Hudi, and other supported sources. Its distributed engine supports petabyte-scale and subsecond graph queries. PuppyGraph completed a 10-hop query across billions of edges in 2.26 seconds in a four-machine cluster.

Agents gain graph compute alongside SQL compute over the same data. Applications choose the engine that fits each question without maintaining a separate graph copy.

As agents reach across more data, access remains tightly controlled. PuppyGraph 1.0 enforces configured row-level permissions throughout graph traversals, including intermediate steps. Single sign-on, role-based controls, and user impersonation tie data access directly to user identity. Credential vending enables PuppyGraph to securely obtain and use short-lived, identity-aware credentials when connecting to data platforms such as Snowflake and Databricks, without requiring users to expose or manage long-lived credentials. Administrative audit logs record management activity, providing greater visibility and accountability across the entire data access path.

Customers describe PuppyGraph’s impact on their AI agents projects, query performance, and business outcomes:

Ran Xia, Engineering Director, Palo Alto Networks said:

“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.”

Xinyu Liu, Senior Staff Software Engineer, Coinbase said:

“With PuppyGraph, standing up a new graph is a schema definition, not a data migration. We connect directly to the tables we already maintain in Snowflake and Databricks and start querying immediately. No pipelines. No second copy of the data, and no data movement. The architecture scales with our existing data platform, even at petabyte scale. And with PuppyGraph MCP, we can query complex relationships in real time using natural language. It has become part of how we work with our data every day.”

Patrick Lu, Lead Architect for Data and AI/ML, Trend Micro said:

“PuppyGraph is the only solution we found that supports Iceberg natively at that scale, with no data loading required. You just connect to the lakehouse underneath, and the cluster itself doesn’t manage any data. Querying it as a graph in place, without duplicating a single row, is what made this viable for us.”

Leon Goldberg, CTO, Sola Security said:

“We run AI agents across thousands of tables and billions of edges a day. Two years ago, I’d have told you the graph side alone made this impractical at this scale. It’s one of those products where you ask: how is this just now?”

“Big data is still an unsolved problem in the world of AI,” said Amy Yin, partner at defy.vc. “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.”

Developers can start free with a Docker command, with no form or payment required, and go from deployment to query in 10 minutes. Visit puppygraph.com to download PuppyGraph or book a demo.

About PuppyGraph

PuppyGraph is the only graph query engine that helps data teams build AI agents that work across 3,000 enterprise tables as one unified graph, without loading data into a graph database. It automates graph schema creation, enables natural language exploration through its built-in AI agent or compatible AI tools via MCP, and helps agents avoid errors from guessing how data connects. Supporting petabyte scale, subsecond graph queries, and complex 10 hop traversals in seconds. Customers include AMD, Datadog, Palo Alto Networks, Netskope, Coinbase, Trend Micro, and more. Learn more at www.puppygraph.com, and follow the company on LinkedIn, YouTube and X.

About defy.vc

defy.vc is a Silicon Valley–based early-stage venture capital firm, currently investing out of defy III. With $700M under management across its funds, defy takes an artisanal approach to company building—backing founders with conviction and providing hands-on support from inception through exit. Our team blends decades of venture experience with entrepreneurial and operating expertise, partnering with the boldest founders shaping the future. Connect with defy at https://defy.vc and @defyvc.

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