Built by engineers tired of stitched-together data stacks.

AI agents act quickly, but their data layer often spans systems built for different workloads. A typical stack might use Redis for session context, Postgres for structured state, a vector database for retrieval, and Kafka for events. Teams then add more infrastructure to keep those pieces synchronized and responsive. That arrangement creates operational overhead, separate failure modes, and difficult questions about whether an agent can read its latest writes. We saw engineers spending substantial time maintaining the connections between systems rather than improving the agent itself. The problem was not a lack of individual tools. It was the cost of making them behave like one data layer.

LaserData is an AI-native data backend built around three primitives: Store, Stream, and Query. It brings the core data operations for AI agents into one system, replacing the usual multi-system stack. We built it from direct experience operating high-throughput pipelines and dealing with the coordination those stacks require. The goal is a simpler foundation for agents that need structured state, retrieval, and live event data together.