Answers
Plain answers to the questions teams ask before buying a context layer — why AI gets company data wrong, and what actually fixes it.
You give AI accurate business context by supplying the four things a model cannot infer on its own: one resolved identity per customer across every system, a single governed definition for each business metric, data current at the moment of the question, and unstructured sources like calls and tickets joined back to accounts. Prompting and model choice do not affect any of the four.
Because the model has access to your data but no agreement about what it means. It does not know which records refer to the same customer, which definition of a metric your company uses, whether the number is current, or what was said on the calls. Lacking those, it infers them per question — so the answer comes back confident, specific, and sometimes wrong.
Partly. MCP gives a model live access to your systems, which genuinely improves reach and freshness. It does not create agreement between those systems: it has no concept of a resolved account key and no shared glossary, so identity and metric definitions are still inferred per query — and inferred differently each time.