OliverAI has announced a pre-seed funding round from Menlo Ventures and Unusual Ventures to scale OliverDB, an analytical data platform purpose-built for agentic artificial intelligence. The company argues that current enterprise analytics infrastructure was not designed for AI agents, which generate continuous and machine-speed analytical workloads. The funding will support a system intended to remove performance bottlenecks and reduce the compute costs that can undermine enterprise AI returns.
Why Agentic AI Demands New Infrastructure
Unlike humans, AI agents create continuous, unpredictable, machine-speed analytical workloads that existing systems were not designed to handle. As AI deployments scale, infrastructure can become both a performance bottleneck and a cost problem, limiting how quickly agents can work while driving compute consumption up faster than the value they create. OliverAI argues this dynamic puts the performance and promised return on investment of enterprise AI at risk.
A Step Change in Analytical Performance
OliverDB is engineered to let AI agents investigate enterprise data in real time while using a fraction of the compute required by existing systems. In internal tests based on ClickBench query shapes, the platform ran hundreds of times faster than ClickHouse on CPUs and thousands of times faster with GPU execution. Praneet Sharma, co-founder of OliverAI, called the result a complete step change, noting that multi-petabyte environments could collapse from thousands of servers to two or even one GPU.
Investor Perspective on a New Infrastructure Category
Menlo Ventures partner Tim Tully said Snowflake and Databricks helped define data infrastructure for the cloud era, while agentic AI represents another fundamental shift. He described Oliver as a new kind of infrastructure designed specifically for the needs of agentic AI. Tully added that the platform combines the performance, efficiency, and control enterprises need to deploy AI agents at scale.
Governance and Observability Without Constraint
OliverAI is pairing speed with governance tools that give enterprises precise control over which systems agents can access and which actions they can take. OliverDB provides observability and governance across enterprise databases and Model Context Protocol servers, with policies for each agent, limits on data and actions, and an attributable record of every interaction. John Vrionis, founder at Unusual Ventures, said Oliver combines the performance, governance, and grounded intelligence required to deploy AI systems in the real world.
Lower Token Costs Through a Model Swarm
OliverAI is also using a model swarm to produce deeper and more reliable outputs while lowering token spend. Instead of relying on one model for a single answer, Oliver runs many smaller specialized models in parallel to test competing hypotheses, examine alternative explanations, and build on useful findings. A conductor model weighs the results and returns the conclusion best supported by available data, or no conclusion when evidence is insufficient, reserving frontier models for work that actually requires them.
Enterprise Availability
OliverDB is available today for enterprise deployments as a managed service or inside a customer's virtual private cloud. Enterprises can test the platform against their own demanding analytical workloads using their own data and queries. The company says this approach allows organizations to measure performance and infrastructure impact directly before broader adoption.
OliverAI's announcement highlights a growing focus on infrastructure purpose-built for agentic AI rather than adapted from human-centric analytics tools. By combining performance, governance, observability, and token efficiency, the company is positioning OliverDB as a foundation for enterprise AI deployments that need to scale economically and securely. With backing from Menlo Ventures and Unusual Ventures, OliverAI now moves to prove its claims in real enterprise environments.