PuppyGraph raises $10M Series A for AI agents

PuppyGraph announced on November 10, 2026 that it raised a $10 million Series A led by Particle Future, with participation from defy.vc. The Santa Clara, California-based company said the round brings its total funding to $15 million and will support engineering and enterprise go-to-market expansion.

The company builds a data engine for AI agents that lets them explore enterprise data as one connected graph without moving or duplicating the underlying data. In its Series A announcement, PuppyGraph said its engine runs multi-hop graph queries directly over supported databases, warehouses and data lakes, without requiring a separate graph database or a copied graph layer.

What does PuppyGraph's software do?

PuppyGraph says its product is designed to turn existing enterprise tables into a unified graph that AI agents can query. The company said its platform can already let agents query across 3,000 enterprise tables as one graph, and that it supports subsecond graph queries for supported workloads.

The startup also said PuppyGraph 1.0 adds AI-assisted graph schema generation, ontology validation, distributed graph execution, enterprise security and agent integration via MCP. According to the company, its AI-assisted schema builder explores existing tables and proposes entities and relationships, which the data team then reviews and approves. PuppyGraph said ontology validation rejects undefined entity and relationship references and returns structured feedback that agents can use to correct their queries.

PuppyGraph’s approach is aimed at companies that want graph-style relationship queries without building or maintaining a separate graph database. The company said it works alongside an enterprise’s existing data platform rather than replacing it, and that it brings graph compute to supported systems including Apache Iceberg, Databricks, Snowflake, ClickHouse, DuckDB, PostgreSQL, Microsoft Fabric and Cloudera.

Who invested in the round?

PuppyGraph said the Series A was led by Particle Future and included participation from defy.vc. The company’s announcement also said the round followed the general release of PuppyGraph 1.0.

SiliconANGLE reported that defy.vc had previously led PuppyGraph’s $5 million seed round in November 2024. On its portfolio page, defy.vc describes PuppyGraph as "the world’s first and only zero-ETL graph query engine" and says Coinbase was the company’s first paying customer.

PuppyGraph has also named AMD, Palo Alto Networks, Datadog, Netskope, Coinbase, Trend Micro, Baker Hughes, Sola Security, HighStreet Insurance Partners and Prevalent AI as customers. In the company’s announcement, Sola Security said it runs AI agents across thousands of tables and billions of edges a day.

Why is PuppyGraph pitching this to enterprise AI teams?

PuppyGraph’s pitch is that AI agents need access to enterprise data that already lives in warehouses and lakehouses, but that moving that data into a separate graph database can add cost, latency and operational complexity. The company said its software lets agents query enterprise data across systems as a graph without moving the data into another database, and that it checks row-level permissions at each hop of a traversal, including intermediate steps.

In coverage from SiliconANGLE, the company said AI agents on its platform can query across 3,000 enterprise tables and return graph query results in under a second. SiliconANGLE also reported that PuppyGraph’s AI-assisted setup drafts a schema from existing tables, after which users can query the graph in plain English, and that the company supports the Model Context Protocol so teams can use existing AI tools.

The company said its software supports compatible AI tools through MCP as well as its built-in agent. PuppyGraph added that, according to the company, its distributed engine can support graph queries at petabyte scale, and that in a separate benchmark it completed a 10-hop query across billions of edges in 2.26 seconds on a four-machine cluster.

What comes next after the financing?

PuppyGraph said it will use the new capital for engineering and enterprise go-to-market expansion. Based on the company’s announcement, the priorities are to reduce the work needed to establish the graph model, make relationship queries practical at enterprise scale and help more teams put AI agents into production.

The company also said its annual recurring revenue grew 7x over the past year, and it highlighted customer use cases that started in one domain and expanded into others. PuppyGraph said AMD began with IT workloads before extending to information security and digital marketing data, while other enterprises moved from observability into fraud detection and from initial agentic workloads into additional business domains.