Thesis — Anko

Problem 1

Dashboards are static.

Operations change daily. Dashboards don’t.

By the time a dashboard is rebuilt or a filter added, the operational moment has already passed.

How Anko Solves It

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Problem 2

BI is not operator-native.

Changing the analysis still means waiting on someone else.

Changing the analysis still means waiting on a data team with other priorities.

How Anko Solves It

Operators ask directly, in plain language — no SQL, no ticket
New cuts and filters happen in the same conversation, not a new sprint
Business logic is already encoded in the ontology, so nothing waits on translation

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Problem 3

Investigations are manual.

Teams still slice, compare, explain, and report by hand.

Every recurring investigation starts from zero — slicing, comparing, and explaining by hand.

How Anko Solves It

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Problem 4

Answers need context.

Generic AI cannot know your metrics, forecasts, targets, and business rules by default.

Generic AI doesn’t know your metrics, forecasts, or business rules — it guesses.

How Anko Solves It

The ontology encodes metric definitions, fiscal conventions, and attribute relationships once
Every answer is grounded in that shared definition, not reinvented per session
The same context follows you across Chat, Workflows, and MCP

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