Described, not inferred

Your AI can only be as good as your business is described.

Your agents have your tables and columns. They don't have your business. They don't know that an active customer excludes the trial accounts, that a return is negative, that a work order and a sales order are different things with the same name. So they answer anyway, confidently, and someone catches it three meetings later.

2.2×more top-tier answers — the same model, grounded in an ontology versus not

21%of tasks solved against real enterprise schemas, where that same model scores 91% on a toy one

30%fewer tool calls to reach the answer, which is what not-guessing costs

Microsoft-controlled A/B, 4,000 responses across 400 questions, Fabric Updates Blog, June 2026 · Spider 2.0, ICLR 2025. A separate controlled study found semantic context accounted for essentially all the significant variance; model choice within tier did not.

What Kenseme builds

A description of your business that software can act on

Describe

A formal model of your business in W3C OWL 2, built from the documents, glossaries and people you already have, grounded in your industry standard — FIBO, GS1, HL7 FHIR, IEC CIM.

Bind

Mapped to your live data — to the warehouse model you have, or to a gold model your data team builds toward. Schemas, pipelines and code in your platform's native dialect.

Govern

Domains, data products, glossary and lineage, kept with the model. Extract what already lives in your catalog rather than rebuilding it. An MCP endpoint exposes all of it to any agent you run.

Authored in an open standard and exportable in full, so it stands up on whatever platform you choose next. Deploys to Microsoft Fabric, Databricks, Snowflake and SQL — your business crosses those boundaries on purpose, and the model describing it should too.

The two-week proof of concept

Two weeks from now you can ask an agent a real question about your business, and trust the answer.

We start every engagement the same way, with a short proof of concept on your own data. You pick one domain — the one where a wrong answer costs the most. We model it, bind it to your live data, deploy it to the platform you already run, and put an agent on top of it. Not a demo on someone else's data. Yours.

Start the conversation

See what this looks like on your own data

Interested in learning more, or ready to talk about a two-week proof of concept? Leave us your details and we'll come back within one business day. The first step is a short conversation, not a commitment — usually about which domain would prove this out fastest for you.

Prefer email? hello@kenseme.com reaches the same place.