AI Readiness
AI Readiness for
Power BI Semantic Models
BI Pixie assesses the AI Readiness of your semantic models, optimizes them, and benchmarks Copilot and data agents to prove they stay correct. The first AI Readiness assessment of each semantic model is free, for up to 500 semantic models on every plan.
Free assessment for up to 500 semantic models · No credit card needed
Three problems between you and production
Your organization has committed to adopting AI, and the question is no longer whether to do it but whether the answers can be trusted. Copilot in Power BI and Fabric data agents generate their answers from the metadata inside your semantic models.
A wrong answer by AI looks like a right one
An agent that lacks context does not report an error. It returns a confident answer that may be wrong, and the first warning is a wrong number in an executive review. BI Pixie measures whether a semantic model can support correct answers before you open it to AI.
How do you get AI Readiness at scale?
Optimizing one semantic model takes many iterations and tests, and you have hundreds of semantic models with a team that can properly curate a few of them a quarter. BI Pixie assesses your mission-critical semantic models and ranks the work by the fields your people actually query, so with AI assisting you optimize each one in minutes instead of hours.
Stay correct as semantic models change
A new measure or a deleted description can break an agent that answered correctly last month, and nothing flags it until a user reports it. You run the same benchmark after every change to your semantic models, and BI Pixie compares it with the run before.
The semantic model is the AI surface
Your Power BI semantic models already carry the business context AI needs, but it was written for people, not for agents. It can take time and many iterations until your data analysts can scope down context and resolve the ambiguity, so AI attention stays on what answers the question.
To help you prepare your semantic models for AI, Power BI released Prep data for AI, which lets you curate the metadata AI needs. But as demand rises to prepare your mission-critical semantic models for Copilot and data agents, you need a way to scale up the effort.
- How do you know which fields to exclude from the AI data schema?
- How do you test AI on the optimized semantic model?
- When do you know the semantic models are ready?
- How can you reduce the iterations and speed up the process?
- Can you scale the effort across business units and hundreds or even thousands of semantic models?
BI Pixie was built to answer these questions.
The AI Readiness Loop
Assess, improve, prove, protect, then repeat
A semantic model drifts as people add measures and rename columns, so keeping it AI-ready is ongoing work.
Assess
Assess the AI Readiness of your semantic models.
BI Pixie produces one AI Readiness score from 0 to 100, read directly from the semantic model definition. The findings are ranked by the fields your people actually query, so you are told what to fix first.
Improve
Fix what holds AI back.
BI Pixie drafts the metadata an agent needs to answer correctly, from descriptions to AI Instructions. Every change is previewed, editable, and applied by you. Drafting uses an AI provider you connect, such as Azure AI Foundry, OpenAI, or Anthropic; assessment never does.
Prove
Test AI on answers you already know.
BI Pixie asks questions whose correct answers are computed live in DAX from your own data, then has the AI answer them. You see each answer, the query the AI wrote, and the correct answer beside them.
Protect
Catch inaccuracy before your users do.
Semantic models change, and a new measure or a deleted description can break an agent that worked yesterday. You run the identical benchmark again, and BI Pixie compares the result with the run before it.
Your BI developers can optimize a semantic model using a variety of tools. Only BI Pixie knows which fields your people actually use, and can scale up that optimization to get you ready for production.
What only BI Pixie can do
You fix what your people actually use first
Anyone can read your semantic model's structure. Only BI Pixie knows which fields your people actually query, so the findings are ranked by what your audience uses rather than by a generic rulebook.
Assessment and proof in one product
BI Pixie assesses the semantic model and then answers whether it is actually better, with a benchmark keyed to your own data.
Test the AI you actually ship
You can benchmark through a generic AI agent to diagnose problems, through the same query engine behind Copilot in Power BI to confirm the result, or through your own Fabric data agent. The generic AI agent is your connected AI provider answering from the semantic model, the way a custom agent would, and every wrong answer it gives is traceable to the query behind it.
You stay in control
Assessment is read-only. Every change is previewed, editable, and applied by you, and assessments never run on your capacity.
Assessment is read-only and repeatable, and every benchmark is graded against answers computed from your own data. No personal data is collected by default.
Benchmarking against regression
Getting a semantic model AI-ready is work you can finish. Keeping it AI-ready through a year of changes requires evidence that nothing has regressed. On the Enterprise plan, BI Pixie also runs AI Readiness assessments on a schedule you set, so drift in the context is caught early.
Baseline before go-live
BI Pixie saves the benchmark questions built from what your people actually use, and you review every question and its computed answer before the first run.
You run the identical benchmark after every change
A single new measure or a deleted description is enough to change the answers an agent gives. Because the questions are identical, the comparison is honest.
Fixed answers or live answers
You can score against the answers saved when the benchmark was created, or recompute them from current data. BI Pixie tells you when the correct answers themselves have drifted, and marks a benchmark that was edited, so you always know whether two runs can be compared.
History you can compare
Every run is kept per semantic model with its change from the run before, grouped by the AI that was tested. Scores from different AIs are never combined into one trend.
The 13 AI Readiness dimensions
BI Pixie reports how each dimension contributed to the score, so you know which fixes are worth making first.
- Description coverage
- Name disambiguation
- Field clarity
- AI data schema
- AI Instructions
- Indexing limits
- Verified answers
- Model structure
- Time window safety
- PII and RLS safety
- Field defaults
- Display folders
- Implicit measures
Getting started
The first AI Readiness assessment of each semantic model is free, for up to 500 semantic models
A first look at 500 distinct semantic models is included on every plan, paid or free. The Free plan also covers one tracked report, one tracked semantic model, and one benchmark run on that semantic model. Paid plans are priced by the number of items you track, with no usage meters and no overage charges. Building a benchmark is included on every plan, and paid plans include more benchmark runs.
Is your semantic model ready for AI?
The first assessment is free for up to 500 semantic models, so you can find out where you stand before you commit anyone's time.