AI Dashboards & Reporting
Dashboards you build by asking. Describe the chart you want in plain language, get it from governed data, pin it — and it keeps itself up to date. Reporting becomes a conversation, not a backlog ticket.
What it is
Most dashboards are built once, by whoever knew the BI tool, and then slowly go stale while everyone screenshots them into slides. An AI dashboard works the other way: you ask a question in plain language, an agent pulls the answer from your governed data and renders the right chart, and one click pins it to a dashboard that stays live. The next question becomes the next widget. The dashboard is built by the people who use it, in the language they already speak.
Why it matters
The gap between 'we have the data' and 'the person who needs the number can see it' is where most reporting time disappears — requests queue behind the one analyst, and by the time the chart ships the question has changed. When the interface is a conversation, that queue disappears. And because every widget reads from the same governed catalog, two dashboards can't disagree about the same number.
What we build
The pattern comes from our own production platform, where dashboards, chat, and agents share one data layer.
- Ask-to-chart: plain-language questions become charts from governed data
- Pin from chat: one click turns an answer into a persistent widget
- Agent-backed widgets that refresh themselves on your schedule
- Live updates pushed to the dashboard as data changes
- Read-only share links and snapshots for people outside the tool
- Full chart vocabulary — trends, funnels, heatmaps, maps, KPI tiles
What it works with
Reads from Data Foundations — the governed catalog, plus SQL and API connectors with read-only scopes — so every chart traces to a source. Widgets run on the same agent runtime as everything else: permissions apply per viewer, and a dashboard can never show someone a number they weren't allowed to query. The research behind the approach is documented in the AI-Native Dashboards and Generative UI tracks.
When you need it
Reporting requests queue for weeks. The same KPI shows three values in three decks. Leadership wants answers on Monday morning, not a BI ticket resolved on Thursday. Or your team lives in chat and the dashboard tool is where questions go to die.
Related learning
A study on conversational, adaptive, living dashboard interfaces — workspaces that begin as a blank canvas with a single conversational input and build themselves in real time as the user asks, persisting widgets, layouts, and memory across sessions.
Generative, context-aware dashboard components whose logic and rendering are defined by natural language prompts rather than hardcoded configurations — runtime-generated analytical surfaces that retrieve, reason, link, and adapt instead of merely displaying.
One trusted set of data your dashboards, your AI, and your agents can all read. Clear owners, freshness targets, and access controls from ingest to query.
A navigable map of every system your data lives in — schemas, documents, code, tickets, events, owners, and permissions — so an AI agent can find the right source and respect the right access boundary.
How an AI agent finds the right document, chunk, or row to ground its answer in — and why the part that matters is the pipeline around the database, not the database itself.