The layer that makes AI answers accurate
The metric definitions, knowledge graph and retrieval layer that take agent accuracy from ~50% to 95%+.
Our approach
We build two layers. The semantic layer defines your metrics once, maps your dimensions and encodes your business rules, so an agent knows what revenue, active customer or margin actually mean. The context layer gives it everything else it needs to reason, from a knowledge graph, a markdown wiki and retrieval-augmented generation over your documents. Together they take agent accuracy from roughly 50% to 95%+.
Semantic Layer
Metric definitions, dimensions and business rules modelled once and versioned, so every agent and analyst gets the same answer to the same question.
Knowledge Graph
Entities, relationships and hierarchies that let an agent reason across the business rather than query a single table.
Retrieval and Wiki
A markdown wiki and retrieval-augmented generation over your documents, so the agent has the operational context, policies and playbooks it needs to answer well.
Evaluation Framework
We measure the accuracy lift with an evaluation harness, so the uplift is demonstrated on your own questions.
What's in the way
An AI agent pointed at a raw data warehouse guesses. Out of the box it answers business questions at roughly 50% accuracy, because it does not know what your metrics mean. A basic semantic layer lifts that to around 80%, and with a mature semantic layer and engineering harness we reach 95%+.
How we deliver
We audit your existing data models and context sources, find the gaps, and build both layers inside your environment. Everything is versioned, auditable and owned by you. We measure the accuracy lift with an evaluation framework, so the uplift is demonstrated on your own questions.
When to bring us in
For any organisation deploying agentic AI or self-serve analytics. This is the work that makes the answers trustworthy. It powers Xavier and any other agent or tool you point at your data.
What you'll see
95%+ agent accuracy
From a raw warehouse baseline of roughly 50%, a basic semantic layer takes you to around 80%, and a mature semantic and context layer reaches 95%+.
Trusted answers, self-serve
Once metrics are defined once, self-serve analytics and agentic answers agree across finance, operations and the data team, so the same question returns the same number.
The layer Xavier runs on
Powers Xavier and any other agent or tool you point at your data, inside your environment and under your governance.
See how this applies to your industry
Common questions
What is a semantic layer?
A semantic layer is the business meaning that sits on top of your data models. It defines your metrics, dimensions and business rules once, so an AI agent or analytics tool knows what terms like revenue, active customer or margin actually mean in your business. Without it, an agent querying raw data guesses.
What is a context layer for AI?
A context layer gives an AI agent the knowledge that does not fit in a data model: the documents, definitions and domain logic it needs to reason. Xephyr builds it from a knowledge graph, a markdown wiki-style knowledge base, and retrieval-augmented generation (RAG) over your documents.
How much does a semantic layer improve AI accuracy?
An AI agent pointed at a raw data warehouse answers business questions at roughly 50% accuracy out of the box. A basic semantic layer giving it business context lifts that to around 80%, achievable within a short engagement. With a mature semantic layer and engineering harness, we reach 95%+.
What is the difference between a semantic layer and a knowledge graph?
A semantic layer defines what your metrics and data mean. A knowledge graph maps the entities in your business and the relationships between them. They are complementary: the semantic layer makes the numbers trustworthy, the knowledge graph and the rest of the context layer help the agent reason about everything around them.
What is RAG (retrieval-augmented generation)?
RAG is a technique where an AI retrieves relevant passages from your own documents before it answers, so the response is grounded in your content and can cite its source. In a context layer, RAG lets an agent pull the right policy, playbook or definition at the right moment rather than relying on what the model already knows.
Ready to transform your data?
Book a call with our team to discuss how data and AI can drive results for your business.