PuppyGraph, a US-based startup developing graph query infrastructure for enterprise artificial intelligence (AI), has raised $10 million in a Series A funding round led by Particle Future, with participation from defy.vc.
The latest funding takes the company’s total capital raised to $15 million. According to PuppyGraph, the proceeds will be used to strengthen its engineering capabilities and expand its enterprise go-to-market operations.
The funding comes as businesses increasingly explore AI agents capable of retrieving information, analysing relationships and generating insights across complex enterprise data environments.
PuppyGraph Targets Enterprise Data Challenges
Enterprise organisations often store information across multiple databases, data warehouses and data lakes. While AI agents can initially work with a limited number of data tables, connecting information across thousands of tables and different systems can become difficult as projects move into production.
PuppyGraph aims to address this challenge through a graph query engine that allows AI agents to explore relationships across enterprise data without requiring organisations to transfer their underlying information into a separate graph database.
“The insights of enterprise data will soon be generated by agents,” said Weimo Liu, Co-founder and CEO of PuppyGraph.
He added that AI agents would need to navigate thousands of tables, understand relationships and retrieve supporting evidence across departments and systems.
PuppyGraph 1.0 Enables Queries Across 3,000 Enterprise Tables
The funding announcement follows the general availability of PuppyGraph 1.0, which the company says enables AI agents to query data across as many as 3,000 enterprise tables through a unified graph.
The platform is designed to work across databases, data warehouses and data lakes while keeping the underlying data in its existing locations.
One of the key features of PuppyGraph 1.0 is an AI-assisted schema builder. It generates graph schemas from existing enterprise tables, which data teams can review and approve before deployment.
Once the schema is configured, AI agents can explore connected data using natural language through PuppyGraph’s built-in AI agent or compatible tools integrated through the Model Context Protocol (MCP).
Focus on Accuracy and Reliable AI Queries
PuppyGraph also includes mechanisms designed to prevent AI agents from making unsupported assumptions about relationships between enterprise data.
The engine rejects undefined relationship references and provides feedback that agents can use to correct their queries. This approach is intended to improve the reliability of data retrieval and reduce errors when agents navigate complex information environments.
For enterprises deploying AI agents in business-critical workflows, the ability to trace relationships and retrieve supporting evidence is an important requirement.
Supports Petabyte-Scale Data Workloads
According to the company, PuppyGraph’s distributed query engine supports petabyte-scale workloads and subsecond graph queries for supported use cases.
In a separate benchmark disclosed by the company, the platform completed a 10-hop query across billions of edges in 2.26 seconds using a four-machine cluster.
The platform integrates with several widely used enterprise data technologies, including:
- Apache Iceberg
- Databricks
- Snowflake
- ClickHouse
- PostgreSQL
- Microsoft Fabric
PuppyGraph also provides enterprise security capabilities, including configured row-level permissions during graph traversals, role-based access controls and single sign-on.
These features are designed to help organisations maintain existing data access policies while enabling AI agents to retrieve information across interconnected systems.
Enterprise Adoption and Revenue Growth
PuppyGraph counts companies including AMD, Palo Alto Networks, Datadog, Netskope, Coinbase, Trend Micro, Baker Hughes and Sola Security among its customers.
The company also reported that its annual recurring revenue (ARR) increased sevenfold over the past year, although it did not disclose the underlying revenue figure.
The reported growth reflects increasing enterprise interest in infrastructure that can help AI systems access and interpret information distributed across large organisational data environments.
Funding to Support Engineering and Market Expansion
With the $10 million Series A round, PuppyGraph plans to expand its engineering team and strengthen its enterprise go-to-market capabilities.
As organisations move from AI experimentation towards production deployments, the ability to connect enterprise data securely and accurately is becoming a key infrastructure requirement.
PuppyGraph is positioning its graph query engine as a solution for this challenge, enabling AI agents to work across existing enterprise data systems without requiring a separate graph database migration.
The latest funding will support the company’s efforts to expand this infrastructure as demand for enterprise AI agents grows.
By : Vanshika Tayal
