AI dashboards
Turn a prompt and live business data into reusable KPI cards, trends, breakdowns, funnels, tables, filters, and analysis.
Includes 25 real Glow chart examples and their personalization settings.
Explore this use caseUse cases
AI dashboards and data pipelines for the tools your team already uses.
Choose a concrete workflow, see the data path, and connect the AI tool you already use. Every result stays in a shared DataUnmess workspace instead of disappearing inside one chat.
Choose a workflow
Dashboards and pipelines built from real sources
AI data pipelines
Import, clean, join, reshape, schedule, and publish data without setting up a traditional orchestration stack.
Use connector-native imports, SQL pushdown, or Python based on the job.
Explore this use caseGoogle Sheets dashboards
Build dashboards directly from a connected spreadsheet and keep every chart tied to the source.
Refresh the dashboard by rerunning the same saved query path.
Explore this use caseGA4 dashboards
Bring GA4 metrics into a focused operating view for acquisition, engagement, conversion, and channel performance.
Seed recent history, schedule daily imports, and backfill longer periods.
Explore this use caseHubSpot dashboards
Track pipeline value, deal stages, owners, conversion, and sales activity from a managed HubSpot import.
Keep CRM entities current with scheduled repair-window imports.
Explore this use caseProduct fit
Need to compare the approach first?
See where DataUnmess fits for small teams, and compare the workflow with traditional BI and orchestration products.
Choose by source
Start with the system that already holds the answer
Use a direct dashboard when the source is ready to query. Build an AI data pipeline first when the data needs cleaning, joins, reusable business logic, or a managed refresh path.
For recurring operational questions, define the metric, row grain, date window, and refresh expectation before choosing charts. For exploratory work, begin with a small source sample, validate the fields and totals, then save the useful result as a dashboard, dataset, or pipeline that the rest of the team can reopen.
The goal is not simply to generate a visualization. It is to leave a source-backed artifact with enough context to explain where the number came from and how it should update.