PuppyGraph raises $10 million as version 1.0 targets enterprise graph queries
The AMW Read
The funding and product launch add a company-level update in enterprise data infrastructure, while the disclosed evidence does not establish broader market impact.
PuppyGraph raises $10 million as version 1.0 targets enterprise graph queries
PuppyGraph raised $10 million in a Series A round led by Particle Future, with participation from defy.vc. The round brings the US company's total funding to $15 million and will support engineering and enterprise sales expansion. The funding follows the general availability of PuppyGraph 1.0, a graph query engine that the company says can connect up to 3,000 enterprise tables without transferring the underlying data into a separate graph database.
The launch places PuppyGraph in the data infrastructure market, where enterprise AI tools need to retrieve information across fragmented systems. Its AI-assisted schema builder proposes relationships between existing tables for data teams to review and approve. Agents can query the resulting graph through PuppyGraph's built-in agent or compatible tools connected through the Model Context Protocol. The company says the engine rejects references to undefined relationships and returns feedback that agents can use to correct queries.
For builders, the approach offers a way to test multi-hop queries against existing databases, warehouses, and data lakes without first copying data into a graph database. Teams should evaluate performance on their own workloads: PuppyGraph reports subsecond queries for supported workloads and a separate 10-hop benchmark completed in 2.26 seconds on a four-machine cluster. The company says annual recurring revenue grew sevenfold over the past year, but did not disclose its revenue base.