We build AI reporting automation on top of the Tableau and Power BI environment you already have. Weekly KPI summaries. Dashboard commentary. Anomaly detection. Executive briefings. Data-quality alerts. The narrative work that runs on top of the BI tool, the weekly summary email, the recurring question, the Monday-morning brief, is what we automate. The dashboards stay where they are.
AI reporting automation is the use of AI to turn dashboards and warehouse data into the things your team actually consumes. Weekly KPI emails, executive briefings, dashboard commentary, anomaly alerts, and data-quality reports. The AI does not replace your BI tool. It runs on top of it.
AI reporting automation uses AI to turn your existing dashboards and warehouse data into the outputs people actually read: weekly KPI emails, executive briefings, plain-English chart commentary, anomaly alerts, and data-quality reports. Thinklytics builds this on top of your Tableau and Power BI, grounded in your metric definitions, with human approval before anything reaches an executive.
AI that reads your existing dashboards and source data, then produces the weekly summary, commentary, anomaly callout, or recurring answer.
A new BI platform. We do not replace Tableau or Power BI. We automate the work your team does on top of them.
A generic AI summary feature. We build for your specific metric definitions, your specific approval workflow, and your specific distribution channels.
A black box. Every commentary or summary is grounded in retrieval from your data with logged sources.
Weekly KPI summary email built from your Power BI or Tableau workbooks. Includes anomaly callouts and last-week comparisons.
Dashboard commentary in plain English under every chart. Explains what changed and why.
Anomaly detection on your KPIs with one-paragraph hypothesis drafts to the metric owner.
Executive Monday-morning brief assembled from finance, ops, and product dashboards.
Recurring-question agent that posts answers to Slack or Teams with source links back to dashboards.
Tableau Server response time after rationalization. $6.2M migration avoided. The clean foundation reporting automation runs on.
Report run time after migrating 140 Crystal Reports to Power BI in 20 weeks. $1.1M a year in licensing saved.
Ad-hoc report requests per week after self-service rollout to 340 clinical staff. $890K in analyst labor saved annually.
An analyst spends 6 hours every week writing the KPI summary email.
There's no anomaly detection or alerting on the metric layer.
Dashboard adoption is low because nobody knows what the chart means.
There's no commentary layer explaining what changed and why.
No. It removes the repetitive parts of their work, like the weekly summary, the recurring question, and the dashboard commentary. They focus on the analysis that needs judgment.
Only if your metrics are defined. That's why we start with a data review. If 'ARR' means three different things in three departments, the agent will reflect that ambiguity. We fix the metric layer first.
What is the difference between this and Tableau Pulse or Power BI Copilot?
Native AI features in Tableau and Power BI work for generic patterns. We build for your specific reports, your specific metric definitions, your specific approval workflow, and your specific distribution channels (email, Slack, Teams, CRM). We use the native features when they're sufficient. We don't replace them when they're not.
We constrain the model to your source data and log retrieval. We require human approval on outputs sent to executives or customers. For internal weekly summaries, we surface confidence and cite sources.
Can this work with Tableau Server, Tableau Cloud, and Power BI Service?
Yes, all three. We use REST APIs and read-only credentials. We don't ship sidecar processes that touch your production environment without your security team's review.
It starts with the reports that drive decisions, then expands. These are the factors that move the effort.
Weekly KPI summaries, per-chart commentary, and executive briefings each add surface to build and maintain.
How your Tableau or Power BI workbooks are structured affects how cleanly narratives can be generated.
Reliable commentary needs metrics defined once, or a semantic layer comes first.
Detection with hypothesis drafts to metric owners is more than a static summary.
Your team writes the same dashboard commentary and KPI emails by hand every week.
You already run Tableau or Power BI and want narrative on top, not a new tool.
You want anomaly alerts with a plain-English hypothesis, not just a number.
You want users to ask questions in plain language: see Agentic BI Implementation.
Your numbers disagree across reports first: start with a Semantic Layer.
You need the dashboards themselves rebuilt: see Analytics & BI.
Design reporting agents with bounded scope and approval gates.
Score your reporting data against the bar production AI needs.
Clean up the environment reporting automation will read from.