Knowledge and agent systems

Concretely: an assistant or agent that is grounded, bounded and measurable, instead of a demo you cannot trust in production.

When this is relevant

• You want an assistant grounded in your own documents and data, not the open web.
• You are moving from a chatbot to agents that take actions, and need to bound what they can do.
• Answers must be traceable and evaluated, not just plausible.
• Data cannot leave your premises, so models have to run locally or air-gapped.

Questions we help you answer

• What should an agent be allowed to read, infer and act on, and what never?
• Retrieval, a knowledge graph, fine-tuning, or a combination, and why?
• How is answer quality measured, and how do we catch regressions?
• Where does a human stay in the loop?

What we do

• Design retrieval and grounding (RAG), knowledge graphs and evaluation datasets.
• Build tool-enabled agents and MCP interfaces, with guardrails and clear integration boundaries.
• Deploy models locally, on-premise or air-gapped when data control requires it.
• Set up evaluation and human oversight so behaviour stays observable.

Typical outputs

• Reference architecture for retrieval, agents and their guardrails.
• Evaluation dataset and a working, measured prototype.
• Integration and oversight plan, including a local or air-gapped option.

What we do not do

• No agent with unbounded permissions.
• No grounding claim without an evaluation to back it.
• No hidden dependency you cannot audit or replace.

Related evidence

Radixia's own website is exposed over a publicMCP interface, documented on thesite engineering page. On the blog,Building a GraphRAG for legal contractsandDesigning proactive AI agentsshow the retrieval and agent work in practice, whileThe Model Context Protocol (MCP)andServerless AI Agents with Amazon Bedrock AgentCorecover the tool and runtime layer these systems depend on.