AI dashboard analysis
AI dashboard analysis for Apache Superset
For people who open a board they did not build — management, stakeholders, adjacent teams. One click: a structured read of what is on screen right now — summary, trends, anomalies, and what to check next — scoped to active filters.
- For management
- Filter-aware
- External LLM
01
Summary
02
What is happening
03
Unusual signals
04
Next checks
Why this exists
The author already understands the board. Everyone else does not.
The analyst who built the dashboard does not need their own charts explained. The friction is when a director or manager opens someone else's board — unfamiliar metrics, filters without context, a dozen charts and no narrative. "What is going on here?" goes back to the author.
We implement an AI panel on the dashboard: external LLM, prompts and context from active filters and visible charts — in your fork. The reader gets a briefing in the product, without a round trip through the analyst or ChatGPT.
Four blocks — summary, what is happening, what looks unusual, what to check next — so they can skim in a minute instead of decoding chart by chart.
In the product vs ad hoc
What changes for readers who are not the board author
The model can be the same either way. The difference is whether management gets a ready briefing on what is on screen — or asks the analyst to translate every time.
| Concern | Ad hoc chat | AI in Superset |
|---|---|---|
| Filter & time-window context | Management asks the analyst for an export or a walkthrough. Region, segment, and date range must be re-stated — easy to drift from what the board shows. | Briefing inherits active filters and the time window. Change the slice, re-run analysis; the reader sees text for the same view on screen. |
| Grounding in chart data | The model sees only what the analyst paraphrased or pasted — often without chart lineage or metric definitions. | Reads the same datasets the visible charts use. Answers cite concrete numbers from the open board. |
| Structured read for non-analysts | Free-form chat or a verbal walkthrough from the author. Format changes every time; without the analyst, dead end. | Fixed four-block briefing: summary, behavior, anomalies, next checks — stable enough for a director to skim alone. |
| Follow-up without losing place | New chat thread, new call with the analyst. Filter context and prior questions live outside Superset. | Follow-up and per-user, per-dashboard history inside the panel. Management clarifies in the thread without leaving the board. |
| Where to go next | "Ask the analyst where to look next" — or manual catalog search. | Related-dashboard chips powered by AI search — neighbors by meaning of the current briefing, not by title tags. |
| Prompt & context | The analyst becomes the translator: explains the board, writes the prompt, picks what to paste into chat. | System assembles context from the dashboard — filters, charts, semantic layer — then calls the model. The reader clicks a button; prompt design is part of the implementation. |
Structured output
Four blocks every analysis returns
The panel is for people who open a board without the author's insider knowledge. Each block answers a question they would otherwise ask the analyst.
- 01
Summary
Headline movement: week-over-week shifts, dominant categories, share-of-mix changes — the paragraph a director reads first, without knowing the board's history.
- 02
What is happening
Behavior across the visible window in plain language: which segment drives volume, where metrics hold steady, where they drift — without chart-by-chart decoding.
- 03
What looks unusual
Anomaly layer: what breaks expectations — conversion drops despite rising leads, a metric outside its band — what a non-analyst might miss.
- 04
What to check next
Numbered checklist of follow-up cuts — so the reader or analyst knows where to dig next, without "what am I even looking at here?"
Scope of work
What we implement
Analyze Data with AI on the dashboard
Analysis trigger on the board — available to everyone with access. Not a hidden command for power users.
Per-user, per-dashboard conversation history
Management returns to a prior briefing on the same board — without asking for a walkthrough from scratch. History stays tied to the user and dashboard.
Related dashboards via AI search
One-click chips to neighboring boards by meaning — when the current briefing is not enough, the reader finds the next board themselves. Composes with AI search when both are in scope.
External LLM, integrated
We connect the model API you use. Context assembly and prompt templates live in the fork; the reader clicks a button — no prompt writing, no analyst relay.
Need the system fixed fast?
Stop shipping decisions on broken numbers.
We audit what is failing, repair the foundation, and work with your team until data is reliable in day-to-day decisions.
- Founders in the implementation
- Clear priorities in week one
- Fixes shipped, not just slides
- Metric ownership made explicit
- Pipeline failures caught early
- BI logic aligned across teams
Book a focused call
Tell us where trust is breaking. We will map first fixes and ownership in one working session.