Agents need a new way to get reliable context
As models get better at reasoning, context is what separates a dependable agent from an unpredictable one. To build the best context layer, teams bolt on multiple systems, stitching together retrieval, memory, and observability.
They burn months of engineering time assembling and maintaining this fragmented stack across multiple vendors and specialists. Despite this, when an agent fails, observability only reacts to the traces; it fails to reach the root cause: conflicting sources and stale context.
These stitched systems also get expensive as agents scale. Agents issue many long queries in a single session against a knowledge base that grows with every user, session, and document. Legacy infrastructure built for human query workloads can’t serve agents economically.
Ninebark is the context platform for stateful agents
Solving these problems well meant we couldn’t bolt on another system, because the existing ones were never built to work as one. We had to rebuild the stack for agentic workloads.
So we built Nexo 1 and Ninebark. Nexo 1 is our retrieval engine, built on object storage to serve agents economically without trading away performance. Ninebark runs on it and unifies retrieval, memory, and observability into a single platform.
You get a reliable context layer with proactive observability that flags conflicting sources and stale context before they reach your agent. Run Ninebark in your own cloud or as a fully managed service, alongside any model.
We’ve built this before
We’re a small team with over a decade in search, infrastructure, and product. We’ve watched context limit agents at scale and built this fragmented stack ourselves more than once. We’re building Ninebark so you can build your product instead of stitching together context.