Why Datapane? / Agentic intelligence platform
Connect your data platforms, dashboards and business knowledge through one governed intelligence layer. Bring more context to the question—and keep the evidence behind the answer.
Connected meaning. Evidence-backed insights.
Differentiators / nine connected capabilities
DatapaneAI operates as an intelligence layer on top of existing enterprise systems, eliminating the need for data migration or replacement of current technology investments.
Goes beyond traditional semantic layers to deeply understand enterprise data, business entities, relationships, lineage, and operational context. Intelligence adapts to each user's role, hierarchy, and business responsibilities, delivering highly relevant and personalized insights.
Automatically builds and deploys enterprise-grade dashboards, BI applications, and AI agents directly from natural-language business requirements and user context, minimizing technical intervention while targeting analytics quality beyond conventional BI tools like Tableau and Power BI.
Enables business users to create, customize, and deploy sophisticated analytics solutions through conversational intelligence, while supporting seamless transitions across existing BI environments, including Tableau and Power BI.
Navigates 100+ interconnected tables across multiple databases, warehouses, and data lakes, enabling complex, cross-system analysis that conventional conversational BI solutions struggle to handle.
Unlike table-centric conversational analytics tools such as Databricks Genie, DatapaneAI's Annie is designed to reason across business entities, relationships, operational dependencies, and causal context—moving beyond SQL generation to deliver enterprise-wide insights and actionable intelligence.
AI agents can evolve alongside frontier model advancements without requiring complete rebuilding or redeployment, helping enterprises continuously benefit from improvements in AI capabilities.
Dedicated data stewardship capabilities continuously build, enrich, and maintain enterprise-wide business context, enabling automated creation and improvement of agents, dashboards, and analytics solutions. The platform becomes increasingly intelligent with every interaction and engagement.
Reduces dependency on multiple BI and AI tools, complex data pipelines, extensive data engineering, and manual dashboard or agent development. This helps lower infrastructure, implementation, and maintenance costs while maximizing returns on existing enterprise technology investments.
Across systems, reports and business knowledge
A business explanation can span a warehouse metric, a dashboard measure, a contract and a human decision. Datapane connects those forms of context rather than treating them as separate investigations.
Catalog Mesh
Connect AWS Glue, Databricks Unity Catalog, Snowflake, Redshift and third-party catalogs into a shared inventory. Follow lineage across system boundaries and preserve curated business meaning when source metadata refreshes.
BI-derived context
Use Power BI and Tableau reports as sources of meaning: glossary terms, measure definitions, schema context and visual-to-field lineage. Carry that knowledge into AI investigation alongside published dashboards and embedded BI.
Ontology & trust
Connect entities and relationships to business definitions. Rank context by provenance, keep disagreements visible, and give steward decisions precedence over AI inference instead of silently choosing a value.
Compounding context
Bring documents, value dictionaries, approved corrections and steward rulings into a shared context resolver. Reuse governed knowledge across discovery, analytics, pipelines and Annie rather than rebuilding it for each surface.
For your role
Explore the Analyst, Engineer, Steward and Executive journeys described in the product deck. These workflows explain where to start; access and approval permissions are defined separately for your organization.
Analysts & BI teams
Find relevant data across catalogs, use existing BI definitions as context, and explore questions with Annie. Turn reviewed findings into dashboards, reports and shared Data Apps.
What you work toward: An analysis your team can review, with definitions and source evidence attached.
Data engineers
Connect sources, describe pipeline requirements and review proposed transformations. Follow code-to-data and cross-system lineage before promoting a change.
What you work toward: A reviewable preparation workflow with dependency and impact context.
Data stewards
Review AI enrichment, curate definitions and arbitrate conflicting assertions. Certify semantic products against ownership, quality, privacy and freshness requirements.
What you work toward: Approved business context that remains distinct from AI inference.
Executives & business leaders
Confirm the interpretation of a question, review summaries and supporting evidence, and see what could not be confirmed. Follow published insights over time rather than treating each answer as a one-off.
What you work toward: Decision-ready summaries with explicit evidence, gaps and review checkpoints.
The recursive trust loop
Super Intelligence coordinates context, ontology, document and structured-data agents. The workflow gives people checkpoints to confirm meaning, redirect the investigation and review supporting claims.
Start with the business question, not the source schema.
Resolve terms against rules, values, catalog meaning and governed assertions.
Review the interpretation, assumptions and unknowns before work begins.
Break the question into testable sub-questions and adjust the scope.
Gather evidence, cross-check claims and expose contradictions and gaps.
Review the evidence-linked output; approved corrections inform the next pass.
Query identifiers, retrieved passages and graph relationships support the findings. Conflicting claims, missing evidence and degraded execution remain explicit. Approved corrections become governed context—not an unchecked rewrite of business knowledge.
Alongside Snowflake, Databricks and your BI tools
Your warehouse or lakehouse remains part of the architecture. Datapane’s focus is the context and workflow spanning catalogs, BI, documents and business decisions—not a requirement to replace every existing tool.
BI, AI and semantic capabilities also exist in other platforms. The useful evaluation is how well the complete workflow connects your particular systems, preserves approved meaning and exposes evidence.
Connector coverage, permissions and enabled capabilities depend on the implementation scope.
Semantic data products
Package measures, dimensions, glossary terms, entity relationships and source lineage into a versioned business product. Certification and subscription workflows connect ownership, quality, privacy and freshness requirements to consumption.
Serve business meaning to BI, analysts and agents. Published semantic products expose governed MCP tools, while version changes and subscriber impact remain part of the product lifecycle.
The wider platform
Eight workspaces connect data and AI work through the same meaning-and-trust layer. Start with a bounded workflow and agree which capabilities your team needs.
Federated catalog search, Data Atlas, source discovery and document exploration.
Conversational pipelines, onboarding and reviewable transformations.
Query Lab, exploration and hypothesis testing against business context.
Annie, Super Intelligence, dashboards, embedded BI and durable Data Apps.
Policy, quality, privacy, lineage and context stewardship.
Dependency-aware releases, source-change impact and recovery workflows.
Model and agent lifecycle, evaluation, budgets and cost visibility.
Semantic data products, subscriptions, stories, clean rooms and MCP tools.
Turn business data into a report people can use.
Build the path from raw data to useful insight.
Make every team work from the same meaning.
Follow the number back to its source.
Ask a business question. Keep the context.
Put it in business context
Explore the supplied solution briefs for examples of where shared meaning and traceable evidence matter. These are solution scenarios, not verified customer performance claims.
Automotive
Bring connected-vehicle telemetry, warranty claims, field service and build records together to investigate emerging reliability patterns and trace them back to production context.
Manufacturing
Bring operational signals, production context and enterprise data together to understand data readiness, asset health, quality and supply risk.
Financial services
Align Risk, Finance, Product and Compliance around governed definitions and evidence-linked answers across existing banking data.
Evaluate the fit
Start with a bounded business question and the people responsible for the answer. A useful evaluation should test context and evidence, not just the fluency of an AI response.
Discuss your requirements ↗Questions before you start
Datapane can sit alongside existing warehouses, lakehouses and catalogs. Catalog federation and the shared context layer connect their metadata and business workflows. Agree connector coverage, access, deployment and data movement requirements before implementation.
The platform includes dashboards and embedded BI, and also captures context from Power BI and Tableau reports: glossary terms, measures and visual-to-field lineage. This lets reporting knowledge contribute to agent investigation instead of being isolated in a dashboard.
Context can include catalog metadata, BI definitions, documents, operational entities, literal business values and approved human decisions. Its provenance and trust rank matter as much as its volume; disagreements should remain visible.
Annie helps teams explore business questions using metric definitions, lineage and operational context. It complements data preparation and reporting; people still review the supporting evidence before making decisions.
Start with a specific workflow: data preparation, inconsistent business definitions, a metric that needs tracing, recurring reporting, or a business question for Annie. The product pages explain the inputs, outputs and review steps for each.
Yes. Use Download PDF in the business applications above or on a solution page. The original brief downloads directly; no email form or email delivery is required.
Your data. Your business context.
Tell us the question, systems and evidence your team needs. We’ll discuss where Datapane fits.
Talk to Our Team ↗