Products

From data
to insight.

Products that help teams report faster and decide with confidence.

Structured Data Ontology

How structured data assets relate across Unity, Glue, Redshift, and Snowflake. Filter, explore, and ask Agent Annie about tables, columns, and join paths.

466tables888relationships7catalogs

live / relationship store

Source

Relationship graph

Live query path

One question.
Every layer of context.

Watch a single prompt become a governed, evidence-backed answer.

Enterprise promptWhat are the top causes of product returns by region?

Agent Layer

Plans the route, selects tools, and decides which evidence the answer needs.

Ontology Layer

Maps business terms to entities, lineage, policies, and relationships.

Context Layer

Enriches the query with governed background from every connected system.

Connected data estate

AWS GlueCatalog

SnowflakeWarehouse

DatabricksLakehouse

RedshiftAnalytics

Core Product Suite

Four products.
One connected experience.

Each product solves a real business problem on its own, and together they create a stronger foundation for trusted analytics and reporting.

Product 01

AI-powered report automation

Turn raw data into dashboards, executive summaries, and ready-to-share reports without slowing teams down with manual reporting work.

Faster reporting for leadership and business teamsClear summaries built from live business dataConsistent reporting across recurring reviews

Product 02

GenAI ETL pipeline building

Bring data together faster with AI-assisted ingestion and transformation across files, systems, warehouses, and other business sources.

Quicker onboarding for new data sourcesLess manual setup for data preparationFaster path from raw data to usable insight

Product 03

Ontology layer

Keep teams aligned with shared business terms, metrics, and relationships so everyone works from the same understanding of the data.

Shared definitions for key metrics and entitiesBetter alignment across teams and functionsMore confidence in reporting and analytics

Product 04

Data lineage mapping

Trace every number back to its source so teams can understand where insight comes from and trust what they share.

Clear traceability from source to metricStronger trust in shared outputsBetter support for governance and compliance

AI Assistant

Ask Annie.
Work through the stack faster.

Annie is the AI chat agent inside Datapane.ai, built to help analysts, business teams, admins, developers, and data scientists move from raw questions to governed answers without losing context.

Annie AI Agent

Ask in plain English. Stay grounded in live business context.

Use Annie to explore metrics, trace definitions, understand lineage, and move from a vague business question to a trusted answer across reports, ontology, pipelines, and operational context.

Ask AnnieMetric contextLineageGoverned answers

Built For Teams

Useful across the roles that actually run data work.

Datapane.ai supports data analysts, business analysts, admins, developers, and data scientists in the same workflow, so Annie can help each role ask better questions against the same governed foundation.

Data AnalystsBusiness AnalystsAdminsDevelopersData Scientists

In Action

See Datapane.ai
in action.

Explore how the platform brings reporting, context, and operational visibility together in one guided experience.

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Insight Autopilot

From a question to a deployed, governed data product - run by 8 specialist agents

LiveActivity 4Save sessionSessionsAnnie

AIWhich customers are most likely to churn next quarter?

session: 5123af2d8b1e

Ask a question to begin

Type a business question above and the agents will interpret it, discover the right data across your catalogs, plan the joins, sample, chart, and prepare a governed output - one step at a time.

AINot sure what to ask? Let's build a questionfrom your catalog

1 · Focus->2 · Question

What do you want to learn about?

Governance1

Cost1

Data quality1

Show all

What are the top causes of product returns by region?

Show monthly sales trends across channels

Which customers are most likely to churn next quarter?

1 of 8 complete - 7 to go

IStep 1: InterpretUnderstand the question

Interpret Agent

Interpreted Intent

Identify customers at highest risk of discontinuing their relationship with the business in the next quarter

Sub-intents

Predict churn probability scores for each customer

Identify key behavioral or engagement signals that indicate churn risk

Segment customers by churn risk level (high, medium, low)

Determine the time frame and leading indicators specific to next quarter

Prioritize customers for retention intervention based on churn likelihood

AIAsk the agent to change this step

e.g. treat churn as 30-day inactivity

Chain of Thought

Parse Request

Reading the question: "Which customers are most likely to churn next quarter?". I read this as: Identify customers at highest risk of discontinuing their relationship with the business in the next quarter.

96%

Classify Framing -> Predictive

This is forward-looking, so the output should be a scored / projected result rather than a historical aggregate.

90%

Resolve Metrics & Grain

Pulled the measurable terms out of the question - metrics: churn rate; break-downs: customer. I'll look for these when I scan the catalog next.

84%

Decompose -> 5 Sub-intents

Splitting the question into: (1) Predict churn probability scores for each customer; (2) Identify key behavioral or engagement signals that indicate churn risk; (3) Segment customers by churn risk level (high, medium, low); (4) Determine the time frame and leading indicators specific to next quarter; (5) Prioritize customers for retention intervention based on churn likelihood.

88%

Agent Insights

Predictive, not descriptive