AI Readiness and Adoption.
From Power BI to AI Agents at Scale.
AI readiness for hundreds of
semantic models, not one at a time
Curating one semantic model for AI takes many rounds of edits and tests. BI Pixie assesses your whole estate, ranks the work by the fields your people actually query, and proves the result with a benchmark scored against your own data.
Free assessment for up to 500 semantic models · No credit card needed
AI Readiness scores across your semantic models
See it in action
Watch the BI Pixie demo
See how BI Pixie adds Pixies, invisible tracking pixels, to your Power BI reports, and turns what they record into adoption, engagement and satisfaction insights.
Key Features
See How Reports Are Used, and Make Semantic Models AI Ready
BI Pixie adds Pixies, invisible tracking pixels, to the reports you choose. What they record shows you how each report is really used, and it refines the business context AI receives, so the attention your users give your reports makes AI more accurate.
Know when your semantic models are ready for AI
BI Pixie assesses the AI Readiness of your semantic models across 12 dimensions and names what is missing, before you go live with Copilot and data agents.
Optimize your semantic models for AI
BI Pixie optimizes the metadata AI needs, drawing on AI assistants, visual-level usage data and lineage to reduce ambiguity. AI drafts the descriptions and instructions, and you approve each one before it is written.
Benchmark Copilot and data agents for accuracy
BI Pixie generates sets of questions and answers to test Copilot or your data agent. It calculates every correct answer from your own data, and can test your AI to detect inaccuracies before your users do. You run the same benchmark after every change to your semantic models.
Know which reports are used, not just opened
You see the real audience for each report, how often they come back, who stopped coming, and which reports nobody uses, with dozens of adoption and attrition metrics that Power BI's usage metrics do not carry.
See what people do inside a report
You see which visuals your people use, which they pass over, and how long they stay, down to every click, filter, slicer, bookmark and link. BI Pixie then ranks your report designs by complexity, usability and passivity, and shows their impact on engagement.
Hear from your users inside the report
Your users rate the report and leave feedback inside it, with thumbs up and down or smile and frown, and an embedded survey measures satisfaction and NPS where they work.
Usage is what makes AI more accurate
You cannot curate hundreds of semantic models by hand, and a generic checklist spends your effort on fields nobody queries. BI Pixie uses what the Pixies record to refine the business context AI receives, so the work goes first to the fields your people actually use.
Detect RLS flaws, and see who saw what
BI Pixie records the role applied to each user as they read a report, and shows you who saw data that should have been hidden and who was blocked from data they should see. Audit user activity across reports and support your data governance policies.
Show Finance what BI returns
BI Pixie measures the return on your Power BI investment with adoption, engagement, CSAT and NPS across thousands of reports, and shows which reports you can retire.
Zero Deployment
You sign up, connect your Power BI workspace, and add Pixies in minutes. There is nothing to deploy, because we handle the infrastructure.
Your Data, Your Control
BI Pixie collects no personal data by default. Your data is kept in isolated storage per customer, encrypted in transit and at rest, and report content never passes through the BI Pixie cloud. You set your own retention period and can delete any user's data yourself.
Enterprise Scale
BI Pixie is built for large organizations. You can assign licenses to business units for chargeback to cost centers, give each team its own isolated storage, and scale as your Power BI portfolio grows.
Published on Microsoft Marketplace
Microsoft ISV Partner · Leading Publisher of Power BI Apps
Use Cases
AI Is Here, and BI Challenges Just Got Bigger
Which dashboards should AI replace?
- Scenario: Your leadership expects you to have a plan for AI, whether it is Copilot in Power BI, agents in Microsoft Fabric, or standalone AI tools. The question is no longer whether AI will change your BI portfolio, but which reports to augment or retire first.
- Challenge: You have hundreds or thousands of Power BI reports and no clear way to prioritize which ones are candidates for AI-driven experiences. Some reports may be heavily used but simple enough for an AI agent to replace. Others may be underused because they are hard to navigate. Without engagement data you are guessing, and a wrong guess means wasted AI investment or disrupted workflows that your users depend on.
- Solution: BI Pixie answers both questions: which reports to keep, augment, or retire, and whether the semantic models underneath them are ready for an agent. From session duration, click patterns, and adoption trends, it shows you where users struggle and which reports justify the AI investment. It then assesses the AI Readiness of the semantic models for Copilot and data agents, so the reports you keep are backed by a semantic model an agent can answer from. Before you commit, BI Pixie can benchmark Copilot or a data agent on those semantic models, grading real business questions against correct answers calculated from your own data. BI Pixie uses the same usage data to sharpen both decisions.
Your Copilot gives bad answers
- Scenario: Your organization turned on Copilot in Power BI, or stood up a Fabric data agent, and told everyone to ask questions in plain language. Leadership is watching, and the licenses are already paid for.
- Challenge: The answers come back wrong, or vague, or confidently made up. People try it twice and go back to the reports. You cannot tell whether the problem is the agent, the question, or the semantic model behind it, and you have no way to prove the difference after you change something.
- Solution: BI Pixie tells you whether the problem is the semantic model, and guides the fix when it is. It assesses the AI Readiness of the semantic model and optimizes it with your approval of every change. Then it proves the result with a benchmark: real business questions graded against correct answers calculated from your own data, so you can show your leadership that the answers improved instead of asking them to trust that they did.
Your agent is in production
- Scenario: Leadership wants Copilot and data agents in production, and not on one semantic model but across your estate. Testing by hand cannot keep up: every question you check yourself slows the rollout, and every one you skip is a chance for a wrong answer to reach the business.
- Challenge: An agent that lacks context does not report an error. It returns a confident answer that may be wrong, and a wrong answer looks exactly like a right one until someone who knows the number reads it. An agent that answered correctly last month can also be broken by a new measure or a deleted description, and nothing flags it until a user reports it.
- Solution: BI Pixie generates benchmarks at scale: sets of real business questions with correct answers calculated from your own data, so you can test Copilot or your data agent properly before go-live and catch inaccuracies before your users do. You run the same benchmark after every change to your semantic models, and BI Pixie compares each run with the one before it, so a drop in accuracy shows the day it happens.
The report was opened. Was it used?
- Scenario: You track your Power BI reports with the built-in usage metrics. View counts look healthy, the viewer lists are full, and every adoption review starts from the same slide of opens per week.
- Challenge: In a view count, a five-second glance and a real working session look the same. Everything that separates the two happens after the page loads: clicks, filters, drill-through, time spent. Power BI counts the page view and stops there, so none of that behavior appears in usage metrics. Power BI also leaves out the reports you embed in your own application and the reports you publish to web.
- Solution: BI Pixie runs alongside usage metrics and captures what they leave out. You see how each report is actually worked with: what people click and filter to, how long they stay, and how satisfied they are, everywhere the report runs, including the Power BI service, the Power BI mobile apps, Power BI Embedded, and publish to web.
Where is the ROI on your Power BI investment?
- Scenario: Your organization invested seven figures this year to deliver Power BI reports, counting license costs as well as the headcount of contractors and internal staff.
- Challenge: The return on investment is not clear, and it is not simple to measure across thousands of reports. Page views alone do not give you the picture. Without clear ROI measurements, next year's budget is at risk.
- Solution: BI Pixie measures the ROI of thousands of reports with engagement metrics that include monthly active users, average interactions per session, and session duration. With those numbers you can plan next year's areas of investment and allocate the BI budget across your organization.
Usage tracking for Power BI Embedded
- Scenario: You offer a web app to your customers with Power BI embedded. The analytics inside it are part of what your customers pay for.
- Challenge: Everywhere else in your app you can see how customers behave. Inside the embedded report you cannot: there is no equivalent of a web analytics tool, so you do not know where customers spend their time or what they click. Without that visibility you cannot tell what to improve, and your business depends on the analytics inside the app.
- Solution: BI Pixie gives you usage and engagement data from inside the embedded report. You can measure clicks on bookmarks and slicers, see what your customers filter to, and see where they drill through for more detail. That tells you where to invest to make the app more valuable to them.
The migration that never ends
- Scenario: You supervise a multi-million, multi-year plan to migrate tens of thousands of legacy and modern BI reports to Power BI. The project is underway, and the delivery is gradual.
- Challenge: Business stakeholders keep sending change requests for many weeks after the acceptance test is complete. Your BI teams and vendors spend too much time on maintenance instead of moving to the next migration cycle. They are behind schedule, and the entire migration is at risk.
- Solution: BI Pixie shows how much the reports your BI team delivers are actually used, so you can prioritize maintenance work on evidence and plan the next phases of the migration. When your BI teams deliver semantic models and the line-of-business teams build the reports on top, BI Pixie shows you which of those reports are working and which need your team's attention.
How usage tracking works
Three steps to knowing how your reports are really used
Add Pixies to your reports
BI Pixie adds Pixies, invisible tracking pixels native to Power BI, to the reports you choose. They record what people do inside each report, down to the visual.
Users interact normally
As users open, click, filter, and explore your reports, BI Pixie captures each page view, click, and filter silently in the background, wherever you publish those reports. No personal data is collected by default.
Analyze in the Dashboard
BI Pixie sets up its dashboard for you, and there you analyze adoption, engagement, and satisfaction across the reports you track.
Why BI Pixie is different
BI Pixie grounds AI Readiness in how your reports are really used
An agent that lacks business context does not report an error. It returns a confident answer that may be wrong, and the first warning is a number nobody recognizes in an executive review. Your Power BI semantic models already carry the context AI needs, but it was written for people rather than for agents. BI Pixie assesses their AI Readiness, optimizes them, and benchmarks Copilot and data agents to prove they stay correct. It draws on what the Pixies record, so the business context is refined first for the fields your people actually use, and every correct answer in a benchmark is calculated by BI Pixie from your own data.
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.
Deployment
Choose how you deploy
You can start on our hosted cloud, run BI Pixie natively in Microsoft Fabric, or host it entirely yourself. BI Pixie itself needs only Power BI, and the Fabric option is there if you already run Fabric. Whichever option you choose, your data is encrypted in transit and at rest, kept isolated from other customers, and you choose where that data lives.
BI Pixie Cloud
We host everything, so there is no infrastructure to deploy. Connect your Power BI reports and start tracking in minutes.
BI Pixie Workload in Microsoft Fabric
Run BI Pixie natively inside Microsoft Fabric. You add it from the Workload Hub with no infrastructure to deploy, and BI Pixie sets up what it needs in your own workspace: a lakehouse, a Direct Lake semantic model, the BI Pixie Dashboard, and the notebooks that process your engagement data. When you want a Fabric data agent, BI Pixie creates one for you from a single button.
BI Pixie on Azure
Deploy BI Pixie in your own Azure tenant. Your telemetry and report data stay there, governed by your own security policies and compliance requirements, and BI Pixie never receives them.
BI Pixie on Power Platform
Deploy a lightweight version of BI Pixie on Dataverse and Power Automate. This option suits teams that have already invested in the Microsoft Power Platform.
Free Plan
What's included in the Free plan
Every BI Pixie account starts free, whether you use BI Pixie Cloud or the Fabric Workload. No credit card is required.
- A free AI Readiness assessment for up to 500 semantic models The first assessment of each semantic model is free and does not use up a tracked item. On the Free plan you can run up to 10 assessments a day.
- 1 tracked report and 1 tracked semantic model The two allowances are separate, so you can track usage of one report and prepare one semantic model for AI at the same time.
- 1 AI benchmark run on your tracked semantic model More runs are included with a paid plan. You can generate benchmarks without running them, to evaluate the value you can get.
- Community docs, and MCP access for connecting AI agents
The Free plan is a plan in its own right, not a trial with a countdown.