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ConsultingPower BIFabricAI — Applications & Agents
AI — Applications & Agents

AI Agents & Apps Built on Your Governed Data Model

Get Copilot, agents and Power Platform apps that answer in your business vocabulary. Every result traces back to a measure in your semantic model.

Get in Touch Start With the Model

Why Pilots Stall

Four Reasons AI Pilots Stop After the Demo

Most pilots work in the meeting and stop being used within weeks. The causes sit in the data model.

It Aggregates What Should Not Be Aggregated

On raw tables, an agent sums stock levels across weeks or averages percentages. The result looks plausible and is wrong.

It Does Not Know Your Vocabulary

Revenue means net sales after returns in your company. The model exposes a column called Amount and three helper measures, and the agent picks one.

Nobody Can Verify the Answer

Users will not act on a figure they cannot trace to a measure. Unverifiable answers get used once.

It Was Built for a Demo

Open-ended assistants impress in meetings. Agents that last are scoped to one analysis a team already runs every week.

Our Approach

Model First, AI Second

Your KPI definitions already live in the semantic model. Agents built on that layer inherit every definition, and each answer resolves to a measure.

Raw tables
Amount · Qty · Flag3
Agent
Fluent, unverifiable, wrong
Governed model
Net Sales · OTIF · Coverage
Agent
Traceable to a measure
No agreed definitions
Definitions written once

The same agent on two foundations. With a governed model, every answer resolves to a defined measure.

What We Deliver

Four Ways to Put AI to Work on Your Reporting

Copilot Readiness

Prepare your model for Copilot: clear names, descriptions and synonyms, hidden helper columns, and rules for what must never be aggregated. Your reports improve too.

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What You Get

  • Model naming, descriptions and synonyms
  • Aggregation rules and non-additive measures flagged
  • Model surface reduced to what users should ask
  • A tested question set with verified answers
Copilot — answering on the model
[SCREENSHOT — COPILOT ANSWERING ON THE MODEL]Add one real question, the answer and the measure it used.

Purpose-Built Agents

Automate one analysis your team already runs on a schedule: a weekly performance review, a forecast variance analysis, a supplier scorecard.

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What You Get

  • One agent, one job, defined with the team that runs it
  • Answers grounded in the governed model
  • Every figure traceable to the measure behind it
  • An evaluation set to test every change

Applications & Automation

Capture and action data next to the report: a root-cause form beside the KPI, an approval that routes itself, a write-back that updates the model.

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What You Get

  • Power Apps built against the same model
  • Power Automate flows for approvals and routing
  • Write-back that keeps the model authoritative
  • Embedded inside the report

Anomaly Detection & Alerting

Alert teams when a KPI moves, with enough context to act, instead of finding out at the monthly review.

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What You Get

  • Thresholds defined with the people who act on them
  • Alerts that carry context
  • Tuned against history to limit noise
  • Delivered where the team already works

The Path

Prove One Use Case Before You Scale

Start with one narrow build and a measurable before and after. Extend only when it changes how the team works.

01

Qualify

Pick the recurring analysis worth automating and check whether the model can support it.

Use CaseModel Check
02

Prepare & Build

Prepare the model, write the evaluation set, then build the agent or app.

Model PreparationEvaluation SetBuild
03

Run & Decide

Run it with the team for a full cycle, measure against the evaluation set, then extend, adjust or stop.

Live CycleMeasured ResultDecision

Find Out if Your Model Is Ready for AI

Book an intro call. We review your semantic model and list what to fix before deploying Copilot or agents.

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FAQs

Answers to what business, IT and security teams ask first.

Does our data leave our tenant?

No. Work stays in your Microsoft tenant under your existing security model. Row-level security applies, so an agent only returns what the user asking is allowed to see.

How do we know the answers are right?

Answers resolve to measures you already trust. We write an evaluation set of questions and expected answers before the build and test against it after every change.

We are not on Fabric. Can we still do this?

Yes. Copilot readiness and most agent work run on the semantic model, which does not require Fabric.

What is the first step?

Copilot readiness. It is the smallest project, it improves your existing reports, and it shows how far the model is from supporting agents.