Use case · Business intelligence

Ask for the number. Get the trend.

The warehouse extracts and the monthly packs you already publish sit in the project. A manager asks in plain language. The answer is the figure, the period, and the report it came from. A person still owns the decision.

Self-serve questionsThe report namedTrends beside the number

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EazyAI · Business IntelligenceSample

Revenue by region for Q3, and how it moved versus Q2.

Q3 revenue

From finance_q3.csv · not estimated

North · 42%

West · 31%

South · 27%

North +6% vs Q2. West −2%. South flat. Q3 board packQ2 board packSame definitions in both packs

What about product line?

Product line is not a column in the Q3 extract. The answer stops there.

What changes on this desk.

The screens above are a sample of the workspace. They play on their own. Select a step to hold it.

The number lives in a ticket to analytics.

Every regional cut becomes a request. The deck is a week old by the meeting. Two teams quote two versions of the same month.

The question hits the pack you already published.

EazyAI answers from the extracts and reports in the project and names which one. A trend sits next to the figure. If the cut is not in the data, the answer says it is not there.

How EazyAI is used here.

Four moves. The sources are yours. The answer names them. A person keeps the decision that matters.

01Publish the extracts you trust

The csv, the warehouse unload, the board pack. They stay on your network.

02Ask in the words of the meeting

Region, period, versus last quarter. No ticket required for a cut that exists.

03See the figure and the source

The number names the file and the definition.

04Stop when the cut is missing

A dimension that is not in the data is reported as missing.

What you can show a buyer.

The working shape of this use case. Live in two to three days on the sources you already have.

The meeting question

Region, period, and versus-last-period, answered from the pack.

One version of the month

Both the figure and the comparison cite the same published files.

No invented slice

If the column is absent, the answer says so.

LiveAnswers from the latest pack in the project

CitedFile named on the figure

SameOne definition for the room

The file stays.

The project runs on your premises or your private cloud. Dual RAG cites the passage. The model you serve is the one you register. A demo is the way onto a first workflow.

Operating proof for warehouse metrics.

Natural-language access to metrics with Dual RAG grounding and exportable explanations.

NL → metricAsk for the number in plain language

GovernedAccess follows your warehouse rules

ExplainablePulls you can audit

Warehouse-native. Not a shadow spreadsheet.

The question hits the metrics you already trust. The answer names the measure and the filter. A person still owns the decision that follows.

NL → metric

Ask for the report or the trend without writing SQL by hand.

Governed access

Roles and row rules stay with the warehouse you run.

Explainable pulls

Every number can show how it was retrieved.

Before you book.

What analytics leadership asks before a first BI agent goes live.

Does this replace the warehouse?

No. It sits on top of the metrics and definitions you already govern.

Can it invent a KPI?

No. Silent definitions stay silent. The agent does not invent a measure name.

Who exports the number?

A person with access. The agent prepares the pull and the explanation.

Other desks.

Same platform. A different set of sources and a different person in the loop.

See this use case on your files.

Book a demo. A first workflow in two to three days. Your data stays where it is.

Book a demo