Collaborative analytics for modern data teams

Turn questions into data workflows.

Explore data with SQL and Python, build interactive dashboards, and let AI help plan the work inside one collaborative workspace.

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Built for analysts, operators, finance teams, product teams, and data leaders who need shared context.

Growth workspace / Revenue review

Question

Compare net revenue with acquisition cost and explain the biggest change.

AI plan

Use monthly growth tables
Join spend and orders
Run SQL, inspect dataframe, chart trend

SQL block

select month, net_revenue, acquisition_cost
from mart.monthly_growth
where month >= date '2026-01-01'
order by month;

monthrevenuecost

Jan$42k$13k

Feb$51k$15k

Mar$64k$18k

FindingNet revenue grew faster than acquisition cost after the pricing experiment.

Mara

Looks ready to share.

One place for the entire analysis

Ask, build, run, and share without losing the thread.

Ask
Start with a business question or a technical task.
Build
Combine SQL, Python, inputs, text, and visualizations.
Run
Execute through governed data sources and notebook runtimes.
Share
Publish dashboards and interactive data apps for the wider team.

Stop stitching the analysis together across five tools.

Questions start in chat, queries live in one tool, Python lives in another, and the final dashboard ends up somewhere else. Underdata consolidates the workflow into one collaborative document without asking teams to give up SQL or Python.

Fragmented workflow

Notebook
Dashboard tool
Spreadsheet
SQL editor
Chat
Documentation

With Underdata

One collaborative document
Shared analytical context
Governed execution
Interactive result
Visible history
Reusable workflow

Executable blocks

A document that can actually do the work.

A single Underdata page can combine narrative, code, inputs, outputs, and app layouts so the work behind a metric stays attached to the metric itself.

Revenue review

Combine the question, assumptions, code, outputs, and owner comments in the same shared page.

SQLselect month, net_revenue from mart.growth

Pythonforecast = df.rolling(3).mean()

Chart

Controls

Rich text
SQL
Python
Charts
Tables
Dropdowns
Date inputs
Metrics
Tabs
Layouts

AI that works inside your analytics workflow

Ask for the outcome. Keep control of the process.

Underdata AI can understand document context, consider available schemas and dataframes, draft or edit SQL and Python, ask clarifying questions, propose a plan, apply approved document operations, execute through the native runtime, and evaluate results.

1User request
2Clarification
3Analysis plan
4Approval
5Document operations
6Execution
7Evaluation

User

Compare monthly acquisition cost with net revenue and explain the largest changes.

Underdata AI

I found spend and order tables. Before building the analysis, should refunds be deducted from net revenue?

Proposed plan

Confirm revenue definition.
Create a SQL block that joins spend and orders by month.
Save results as a dataframe for Python inspection.
Add a chart and call out anomalies for review.

AI proposes. You approve. Underdata executes.

See how Underdata AI works

Dashboards and data apps

Build more than static reports.

Turn document outputs into dashboards, reports, internal tools, interactive data apps, and scheduled operational views that business teams can use without editing SQL or Python.

SegmentDate range

Net revenue$64k

Active accounts842

Run statusCurrent

RegionStatusOwner

NorthOn trackOps

SouthReviewFinance

WestOn trackProduct

Governance

Built for work that needs to be trusted.

Keep permissions, executions, and AI-generated changes visible and traceable across shared analytical documents.

Workspace permissions

Controlled data source access

Auditable AI actions

Execution lineage

Scheduled runs

Document history

Environment and secret management

Structured workflows

One platform for different analytical workflows.

Start with the workflows teams usually need first, then expand into a governed workspace as adoption grows.

Exploratory analysis

Investigate questions with SQL, Python, and visual feedback in the same document.

Recurring reporting

Schedule documents and keep reports updated without recreating the workflow.

Interactive dashboards

Turn analysis outputs into interfaces that business teams can explore.

AI-assisted analytics

Move from an ambiguous request to an approved and executable analysis plan.

Book a demo

Bring your data work into one shared workspace.

Build analyses, run code, create dashboards, and collaborate without losing the context behind the result.

Product access and deployment options are discussed directly with each team.