Connect
Connect documents, databases, APIs and applications without surrendering control of where they live.
Context infrastructure for AI agents
Give agents the context to understand, reason and act.
Cherragon turns scattered knowledge, data, memory and operating rules into structured context—ready for every agent at runtime, in the environment you control.
See how it worksRuntime context / 01
01Knowledge
02Memory
03Policies
Relevant context
AgentContext ready
Connected sourcesDocs · Databases · APIs · Apps · Events
02 / Our research
Better models alone will not make enterprise AI reliable.
The knowledge needed to act is spread across documents, systems, decisions and working practices. Retrieving related text is only a beginning. An agent also needs to understand what the evidence supports, how information connects, what has changed and which rules apply.
Our research explores how to represent and deliver that knowledge without losing its meaning—and how to measure whether it helps agents make better decisions. We see context as something to engineer, test and improve, not simply fit into a prompt.
What agents need
01Knowledge
02Memory
03Skills
04Policies
05Retrieval
06Evaluation
03 / How it works
From fragmented information to context agents can use.
One governed path from your systems to every agent that needs them.
Understand
Process, structure and index knowledge, memory and policies inside the deployment you control.
Deliver
Assemble task-relevant context for each agent exactly when it is needed.
04 / Our approach
We build from real information and real tasks.
Our approach connects knowledge representation, retrieval and evaluation in a feedback loop: preserve the source evidence, test the context an agent receives, investigate failures, and improve the system that produced it. Teams should be able to inspect, revise and version their context as their understanding grows.
Use your data where it is. Cherragon brings this infrastructure into your environment, with explicit control over where information is processed, stored, indexed and sent.
External embedding and model providers remain an explicit customer choice, never a hidden assumption.
CherragonControl plane
DeploymentsConfigurationFleet healthReleases
Coordinates software and fleet state—not your source data.
Your environmentCustomer data plane
Source dataProcessingIndexesRetrieval
Context is built and used where your data already lives.
05 / Our vision
An organisation should become more capable as its knowledge grows.
We envision a future where a team’s experience does not disappear between projects, and adopting a new agent does not mean teaching it the organisation from scratch. Reviewed decisions, validated skills and operating knowledge should become foundations that people and agents can build on.
A lesson from one workflow could improve another. A policy change could reach the agents whose work it governs. Years of accumulated expertise could remain useful as people, systems and models change.
Our ambition is to make that collective knowledge a capability organisations can own, develop and put to work.
Building now · Launching soon