An answer needs its definitions.
A number becomes useful when you know what it includes, how it is measured, and which period it describes. Two answers can look similar while referring to very different things.
Whiskers is designed to carry those definitions into an AI-assisted conversation. Approved measures, supported comparisons, and relevant caveats help keep the answer tied to a shared understanding of the business.
In practice
Designed around a grounded answer.
Ask a question
An AI assistant receives a business question that needs approved analytical context.
Resolve the meaning
Whiskers provides the relevant definitions and the ways that information can be explored.
Read approved information
Defined tools retrieve information within a deliberate scope, with read-only access.
Return the context
Results carry their scope and caveats. Gaps in coverage or freshness should stay visible.
What is an MCP?
Model Context Protocol (MCP) is a shared way for an AI application to request context or use a defined tool from another system. Whiskers is our organization’s application of that idea to business analytics.
This is developing work. We’re building around a clear boundary: approved information, deliberate access, and enough context to understand the result. Focused services can also bring specific kinds of information, such as aggregated fan-survey feedback, into an assistant’s workflow.