How to use DataUnmess

DataUnmess is an AI artifacts platform. Unlike AI chatbots that produce text, DataUnmess produces permanent, shareable visual assets — dashboards, flowcharts, data explorations, and lineage graphs — from natural language.

Connect your data (CSV, Excel, Google Sheets, PostgreSQL, MySQL, ClickHouse warehouse, REST APIs, MCP servers) and describe what you want. The AI builds it as an interactive artifact you can save, reload, and share. No AI tokens consumed on reload.

1. 1

   Open the Data tab

   It's on the right-hand panel.

2. 2

   Import your file

   Click + Import and drop a CSV or Excel file, or paste a Google Sheets URL.

3. 3

   Ask a question

   Switch to the Chat tab and ask, e.g. "show me sales by category as a bar chart".

4. 4

   Refine by chatting

   Continue the conversation to add, modify, or remove cards. The AI sees the current dashboard state.

5. 5

   Save

   Click Save in the toolbar and pick a folder. Saved dashboards reload exactly as you left them.

DataUnmess supports multiple data sources. Open the Data tab in the right panel to manage imports and connections.

Saved connections can be tested, edited, reconnected, or disconnected from the Connections panel. Edit mode only pre-fills non-secret fields; passwords and tokens stay encrypted, and blank secret fields keep the stored value. OAuth sources use Reconnect when authorization needs to be refreshed.

File imports

• CSV — drag and drop or click to browse. Parsed instantly in the browser.
• Excel (.xlsx, .xls) — sheet selection, header row picker, and column mapping preview.
• Google Sheets (import) — sign in with Google, paste a sheet URL to import a one-shot CSV snapshot. Works with any sheet in your Drive.

For live data that re-queries on every prompt, use the Google Sheets connection (under Add Connection → SaaS) instead of the import — see APIs & protocols below.

Database connections

• PostgreSQL — Neon, Supabase, or any Postgres-compatible database. Schema discovery shows tables and columns.
• MySQL — MariaDB, PlanetScale, TiDB. Same schema discovery and SQL query support.

The AI writes SQL queries and charts the results. All credentials are encrypted with AES-256-GCM.

Warehouse connections

• ClickHouse — ClickHouse Cloud or self-managed ClickHouse over HTTP(S), with schema discovery and SELECT query support. ClickHouse Cloud requires HTTPS on port 8443; disable HTTPS only for local or self-managed HTTP.

APIs & protocols

• REST API — any JSON endpoint. Configure base URL and auth (Bearer, API key, Basic).
• MCP Servers — Model Context Protocol for AI-powered external tools and data sources.
• GitHub — connect repositories to analyze code, technologies, and architecture.
• Google Sheets (live) — OAuth-authorized connection that lets the AI list spreadsheets in your Drive and read ranges or whole sheets on every request. Refresh tokens are stored encrypted; access tokens rotate automatically.
• Google Analytics 4 — OAuth connection for aggregate GA4 traffic, page, source, geo/device, and event reports. Ask DataUnmess to import GA4 and it can quickly seed recent data, build the overview dashboard, set up daily refreshes, and offer longer history later.
• HubSpot CRM — encrypted private app token connection for contacts, companies, deals, tickets, products, line items, quotes, calls, emails, meetings, notes, tasks, pipelines, and common CRM associations.

Coming soon

Snowflake, BigQuery, and Stripe connectors are in development. They appear in the connector catalog with a "Coming Soon" badge.

Data Pipelines help you bring data into DataUnmess and keep it useful over time. Use them when you want to import data, clean it, save the result, refresh it daily, or load older history later.

When a pipeline writes to a managed dataset, that dataset appears as a pipeline output. Clicking Refresh on the dataset or a dashboard that uses it reruns the upstream pipeline first, then re-queries the dashboard cards. For monthly Google Sheets workflows, keep the same sheet, pipeline, managed dataset, and dashboard; add the new month to the sheet and refresh instead of recreating everything.

Daily refresh and backfill

Most API imports should refresh automatically. DataUnmess starts with the recent data you need now, then can keep the pipeline updated every day. If you need more history, ask for a backfill after the first import is working.

• Daily default - API pipelines can run once per day in your workspace timezone.
• Recent-days refresh - daily runs can recheck the last few days so late-arriving data gets fixed.
• Backfill later - start with a quick import, then add 30, 90, or 365 days of history when you are ready.
• No duplicate clutter - rerunning the same time period refreshes it instead of stacking duplicate rows.

GA4 imports

Ask for your GA4 data and DataUnmess starts with a fast recent import, builds the GA4 dashboard, sets up daily refreshes, and offers longer history after the first dashboard is ready. The default starts with the last 7 days; ask for the fastest path if you only want yesterday.

• Traffic - users, sessions, views, engagement, and conversions by date.
• Pages - page paths, titles, views, users, sessions, and engagement.
• Sources - channel group, source, medium, campaigns, users, sessions, and conversions.
• Geo and device - country, region, city, device category, browser, and operating system.
• Events - event names, event count, users, and key-event metrics.

HubSpot CRM imports

Ask for HubSpot CRM and DataUnmess uses the standard integration bundle: it checks reusable templates, probes which standard CRM objects your private app token can read, skips unavailable or empty objects with a clear reason, imports recent changes with primary-key upserts, schedules daily repair-window syncs, and builds a CRM dashboard.

• Objects - contacts, companies, deals, tickets, products, line items, quotes, calls, emails, meetings, notes, and tasks.
• Sales/service structure - deal and ticket pipelines plus common associations between contacts, companies, deals, and tickets.
• Dashboard - object counts, contacts updated over time, open deal value, deals by stage and pipeline, deal value over time, and ticket status when tickets are imported.

Managing pipelines

Pipelines can be renamed, moved between sidebar folders, run on demand, scheduled, or permanently deleted. Delete actions require an explicit confirmation preview first, including the pipeline or dataset name and affected run history, so stale imports are not removed accidentally.

Before starting a large import, ask "how much of my plan have I used this month?" to check current usage and plan limits.

Prompt examples

Cards

• Drag — hover a card, grab the handle in its top-left corner, and drop it on any grid slot.
• Resize — hover to reveal bottom, right, and corner handles; drag to snap to new widths/heights.
• Settings — the gear icon opens inline config (value format, grid lines, sort order, hide title, etc). Changes apply live — roll back with Ctrl+Z.
• Delete — the × in the top-right.
• Edit title - click the pencil beside a card title, then press Enter or click away.
• Copy name + ID - use the copy button in the top-left corner of the card.

Toolbar

• Save — opens the save dialog, pre-filled with the current name and folder.
• Delete — permanently removes the currently-loaded dashboard (with confirmation).
• Align — resets every card back to its default size/position.
• Undo / Redo — Ctrl+Z and Ctrl+Shift+Z.

Right panel

• Chat — ask anything about your data.
• Recent — browse and restore past sessions.
• Data — imported datasets and a preview of the first 10 rows (horizontally scrollable).
• Style — pick a theme or create a custom one based on any built-in theme.

DataUnmess is AI-first: everything you can do in the UI, you can also ask the AI.

Creating charts

Filtering dashboards

Ask for a saved dashboard-level date or category filter instead of rebuilding every chart. Chat uses the active dashboard; MCP tools target a saved dashboard and call set_dashboard_filter. Dataset-backed query cards re-query with the filter, and unsupported cards are reported.

Modifying existing cards

Changing a chart's type or adding a derived series

Reference cards by the on-card id: N badge — same number on chat and MCP. Safe in-family type changes (e.g. bar → horizontal bar) use update_chart. Type changes that need a different data shape (e.g. bar → combo) are composed as remove_chart + build_chart with the original layout preserved. Derived series (deltas, running totals) are computed in SQL via query_data first, then rendered as a combo (bars for the original series, line for the derived one).

Repointing a dashboard at a new dataset

After a transformation produces a cleaned v2 dataset, redirect every chart on a dashboard to it in one shot — no need to rebuild the layout. The AI calls swap_dashboard_dataset, drops cached chart data so the next reload re-queries the new source, and warns you about any charts referencing columns the new dataset doesn't have.

Reporting DataUnmess bugs

If DataUnmess or one of its MCP tools fails because of a platform issue, the AI can open an internal report with open_bug_report. The report includes the exact failure, expected behavior, reproduction steps, environment, evidence, and screenshots when available.

Themes

Materializing connected data

Working across tabs

The studio has four tabs — the AI tailors its output per tab:

Unlike traditional AI chat, DataUnmess produces visual artifacts — not text descriptions. Every response generates objects that render directly on the dashboard canvas.

Artifact types

• Charts — 20+ types (bar, horizontal bar, line, donut, area, scatter, treemap, funnel, radar, sankey, heatmap, waterfall, geo map, gantt, ridgeline, streamgraph, data-table, and more)
• KPI cards — single-metric highlights with trend indicators and % change
• Analysis panels — written insights with highlight cards showing key metrics
• Dashboards— complete multi-chart layouts from a single prompt ("build me an overview")

Data science exploration

Use conversation to explore your data like a notebook. The AI aggregates, filters, groups, and joins — you see the results as charts instantly. Ask follow-up questions to drill down, pivot, or compare.

Lineage & flows

The Lineage view traces how datasets flow into charts and dashboards as an interactive node graph. The Flow Editor lets you build visual pipelines and transformation diagrams with 9 node shapes (rect, diamond, ellipse, hexagon, parallelogram, cylinder, cloud, document, text) and lucide icons per node. Accepts either structured JSON or a Mermaid flowchart TD|LR block and exports back to Mermaid.

Data pipelines

The Data Pipelines tab is for saved workflows that import, clean, refresh, and backfill data. Pipelines are useful when you want dashboards and reports to keep working from a reliable prepared dataset instead of a one-time upload. See the Data Pipelines section for the day-to-day workflow.

Persistence

Artifacts persist across sessions and survive page reloads with zero AI tokens consumed. Sessions restore from the database. Saved dashboards re-execute local queries against the original data — no AI call needed.

Connect DataUnmess to Codex Desktop, Claude Desktop, Claude Code, ChatGPT, Gemini, Cursor, or Windsurfby pasting our hosted MCP endpoint + an MCP key into your client's config. The AI then calls DataUnmess tools directly — building dashboards, adding saved dashboard filters, running queries, importing GA4 data, scheduling pipelines, generating analysis, and opening internal bug reports when DataUnmess itself behaves incorrectly — with your data.

No install, no clone, no code on your machine. All DataUnmess logic stays on our servers. Remote MCP connections use Streamable HTTP by default; choose SSE only for legacy servers that require it.

{
  "mcpServers": {
    "dataunmess": {
      "url": "https://app.dataunmess.ai/api/mcp",
      "headers": {
        "Authorization": "Bearer <YOUR_MCP_KEY>"
      }
    }
  }
}

Full connect guide →

DataUnmess Memory is the workspace memory layer used by your MCP-connected AI. It stores company context, KPI memory, business glossary notes, and data catalog knowledge as agent-readable markdown. The goal is simple: every useful piece of knowledge you add should make future answers, research, and artifact creation smarter.

How it starts

During MCP onboarding, ask your AI to start DataUnmess Memory onboarding. The AI should ask for your company website, research public pages, and seed the workspace memory with company context, products, glossary terms, standard KPIs, and useful business language.

Where it is used

• MCP answers and research - when you ask business questions, the AI can search memory before guessing from raw data.
• Dashboard creation - dashboards can reuse known KPI names, definitions, formulas, business context, and source-of-truth cards.
• User questions- questions like "what is my Patrimonio Liquido?" can resolve from KPI memory and snapshots instead of rediscovering dashboards.
• Flowcharts - process diagrams can reuse business terms, company products, teams, systems, and operating rules already captured in memory.
• Data pipelines - transformation and pipeline work can add or reuse catalog knowledge such as dataset grain, key columns, trusted use cases, and known data caveats.

How knowledge grows

Knowledge can come from onboarding, manual notes, approved conflicts, dashboards, flowcharts, datasets, and pipelines. New low-risk information is learned automatically. Review is only needed when the AI finds conflicting definitions, competing sources of truth, missing fields, or uncertain data meaning.

Markdown-first storage

DataUnmess Memory is designed to be readable by agents and by humans. KPI memory, KPI snapshots, company knowledge, catalog entries, and review items are written as markdown files under the workspace knowledge folder, with lightweight indexes only for speed.

DataUnmess ships with six built-in themes: Carbon, Candy, Neon, Ocean, Sunset, Emerald. Built-in themes are read-only — you can't edit them directly.

To make your own: open the Style tab → click + New→ give it a name → pick a base theme. The new theme appears under "Custom" with an Edit button that lets you tweak every color. Custom themes are saved in your browser.

The left sidebar lists saved dashboards grouped by folder. Folders can be nested — create a subfolder by hovering a folder and clicking the + that appears.

• Move a dashboard — drag it onto any folder.
• Delete a dashboard — hover and click the ×.
• Delete an empty folder — hover and click the ×.
• Resize sidebar — drag its right edge.

For account access, billing, onboarding, or anything that needs a human reply, email support@dataunmess.ai.

Signed-in users can also use the feedback button in the top bar to send product feedback, bug reports, feature requests, and attachments from inside the app.