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Experimental AI analysis is off by default. It needs AI agents enabled for your organization and the enable-data-app-analysis feature flag, which Lightdash support turns on for Cloud organizations on request. What Experimental means.
AI analysis reads the results a data app has loaded for the viewer’s current filters and tells them what stands out: a headline, a short summary, a list of notable data points, and what the data could not show. For any notable point, an AI agent can then investigate the likely causes using data beyond the page. Nothing about this is written into the app. Lightdash runs the analysis over the queries the app already executed, so every result respects the viewer’s permissions and user attributes, and an app can pick the analysis up and render it in place without a single line of AI code of its own.

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Turn it on

An organization admin enables AI analysis under Settings → Ask AI → General, in the card that lists where agents can be reached:
  • Data apps turns runtime analysis on for every data app in the organization. It sends query results a viewer already has access to, to your configured AI provider. If you have brought your own provider keys, the calls go through your account.
  • Continue investigations in Ask AI lets viewers carry an investigation on as an Ask AI thread. Turn it off to keep viewers at the written explanation.
Both switches are only shown once the feature flag is on. Self-hosted instances add enable-data-app-analysis to LIGHTDASH_ENABLE_FEATURE_FLAGS; see feature flags.

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Analyse a view

Open a data app and click the sparkles icon in the header, labelled Analyse this view. It opens the AI analysis panel on the right.
  1. Pick an agent for investigations. The analysis itself needs no agent; the agent is what investigates a finding later, so any agent with access to the app’s data will do.
  2. Click Analyse this view. The panel reads every query the app ran for the current filters. Queries on tabs you are not looking at count too, as long as the app has loaded them.
When it finishes you get:
  • A headline with the main finding and a summary an executive can read without the charts.
  • Findings, each with a severity of High, Medium, Positive, or Info. High and Medium point at something worth a look, Positive at a good surprise, and Info at context the summary relies on. A finding that refers to one row names the row; a finding about a whole table names only the table. Hovering a finding highlights the chart it came from when the app supports data lineage.
  • Limitations, such as a missing comparison period or truncated results. They are what keeps the summary honest, so read them before acting on the headline.
An analysis with no findings is a normal outcome. The headline says so. If you change a filter or move to a different tab after analysing, the panel flags that the view changed and offers Re-analyse. The earlier result stays visible until you do.

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Reuse

Lightdash stores every analysis with the app version and a fingerprint of the results it read. Opening a view whose results match an analysis someone already ran shows that analysis straight away instead of running the model again, and the same applies when you return to the app later. Findings are shared this way; investigations are personal and are never copied between viewers. Re-analyse always runs afresh.

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Investigate a finding

Click Investigate on a finding. The agent you picked runs in the background with read-only tools: it can search the semantic layer, run queries, read saved charts and dashboards, and read the agent’s knowledge documents. It cannot save content, change anything, or reach external systems. A run is capped at 12 steps and three minutes; if the budget runs out, the explanation says the answer is partial. The explanation arrives as Markdown with a summary, a Possible drivers list with evidence for each, an Evidence list of what was checked, and a confidence line. It stays attached to the finding for you and is restored when you reopen the app.

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Continue in Ask AI

When the organization allows it, a finished investigation has a Continue in Ask AI button. It opens the investigation as a thread in Ask AI with the agent’s reasoning already in place, so you can keep asking questions. The thread stays read-only for its whole life: every later prompt keeps the investigation’s tool set, so it cannot save charts, write back, or start a deep research run, whatever the agent is otherwise allowed to do.

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Show the analysis inside the app

Viewers do not have to open the panel. Ask the app agent to add AI analysis and the app renders the same analysis in place:
  • “Add AI analysis” gives the full set: a summary block at the top of the page, markers on the flagged data points of every chart, Investigate with AI in every data point’s menu, and the investigation card under the chart.
  • “Just an executive summary” gives the summary block alone.
  • “Mark anomalies on the charts” gives the markers, the menu entry, and the card.
The summary block has an Analyse button of its own and shows a Regenerate button once a result exists. Markers are painted on High, Medium, and Positive findings only, coloured by severity, and one metric’s finding never marks another series of the same chart. Hovering a marked point shows the finding under the value, and the point’s menu opens the investigation, or reopens it once one exists. All of this renders nothing when AI analysis is off, so an app built with it works unchanged for organizations that have not turned it on.
Apps built before AI analysis existed do not pick it up on their own. Ask the agent to add it, which creates a new version of the app.

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For app code

If you edit app code locally, two hooks from @lightdash/query-sdk expose the analysis:
  • useInsights() returns the view’s analysis, and useInsights(result) narrows it to the findings on one useLightdash query, with a matches(row, fieldId) helper for marking data points. The template ships ready-made components under src/components/insights/ for the summary block, markers, menu entry, and investigation card.
  • useAiPrompt() lets the app ask its own short question about results it has already loaded, for an author-written takeaway under a chart or an explanation of a clicked row. Answers are plain text. Prompts are capped at 2,000 characters and rate limited to 20 a minute per viewer and app.
Neither hook sends rows anywhere: the app names the query, and Lightdash reads the results from the viewer’s own query history. The app template’s references/ai-analysis.md documents both in full.

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What is sent to the model

  • Analysis sends the query results the app loaded for the viewer, with their field names and the app’s instructions, to the AI provider configured for your organization.
  • Investigation runs the agent you picked, with the same data access that agent has anywhere else.
  • App prompts send the results the app named, and the author’s question.
Results a viewer cannot see never reach the model, because the analysis only ever reads queries that viewer ran. Analyses are stored in your Lightdash database alongside the app.