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    Your business already asks the questions. Genie answers them.

    Databricks Genie turns plain-language questions into governed answers from your own data. Polestar Analytics gets you from first question to production adoption, starting with 60 days on us.

    Genie Kick Start
    A data-smart AI coworker, not another dashboard

    Genie began as conversational analytics inside Databricks AI/BI. At Data + AI Summit 2026 it became a family of agentic products that answer questions, take action, and stay inside the permissions your teams already have.

    Genie One
    The AI coworker for everyday teams. It connects across your data estate and the tools where work already happens.
    • Ask in natural language, get governed answers
    • Native in Slack, Microsoft Teams, iOS and Android
    • Schedules, alerts, document creation, custom skills
    • Connects to other systems through MCP
    Genie Agents
    Genie Spaces evolved into curated, domain-specific agents that complete multi-step work, not just answer questions.
    • Spin up an agent from a single prompt
    • Reasons over documents and files, not only tables
    • Takes autonomous action across business tools
    • Scope it, benchmark it, share it with the team
    Genie Ontology
    The living context graph underneath both. It learns what your data actually means so Genie stops guessing.
    • Extracts meaning from tables, queries, dashboards, apps
    • Ranks definitions by authority, usage and freshness
    • Fed by Unity Catalog glossary, domains and metrics
    • Enforces source permissions on every answer
    Most AI fails on business questions for one reason: it knows the schema, not the meaning

    Your definition of net revenue, active customer, or incremental lift lives in dashboards, wikis, tickets and people's heads. When AI cannot find it, it infers, and inference is where wrong answers come from.

    Genie Ontology solves this by learning your business context continuously and weighing which definitions to trust, using an approach similar to PageRank. The stronger your Unity Catalog semantics, the better it performs. That is exactly the foundation Polestar Analytics builds.

    Databricks Internal Benchmark

    84.5%

    of questions answered correctly on the first attempt, grounded in Genie Ontology
    Strongest general-purpose coding agent 52.4%
    Weakest agent tested 25%
    Source: Databricks internal benchmark, 28-question real-world data-analysis suite, June 2026. Competing agents anonymized. Genie also delivered answers 2x faster than the strongest coding agent.
    What Genie gives your teams
    01
    Answers in plain language

    Business users ask questions the way they would ask a colleague. No SQL, no ticket to the data team, no waiting three days for a number.

    02
    Governed by default

    Every response respects Unity Catalog permissions and source-native access controls. People see only what they are entitled to see.

    03
    Where work already happens

    Genie lives in Slack, Teams, mobile, Databricks One, or embedded in your own applications. Adoption stops depending on a new tab.

    04
    Action, not just insight

    Schedule briefs, generate documents, trigger alerts, and write back to connected systems through MCP integrations.

    05
    Structured and unstructured together

    Agents reason across warehouse tables and the documents, files and knowledge sources that explain them.

    06
    Cost and quality visibility

    Unity AI Gateway governs tools, MCP connections and spend, giving admins one place to watch usage and cost.

    Partnership Program Offer
    $0 Genie Kick Start

    Most Genie pilots stall in the gap between "this looks promising" and "this is live." The runway to prove value usually runs out before the value shows up. So we removed the barrier: as a Databricks Lakebase Launch Partner, Polestar Analytics funds your proof of value.

    $0
    Cost to start
    60days
    Fully funded by Polestar Analytics
    2
    POC Categories
    14
    Pre-Built Offerings
    Included
    Pre-built business applications
    Genie configurations shaped around commercial questions your teams already ask, not blank-slate setup.
    Included
    Real use cases, your data
    We scope to a live business problem in trade promotion, pricing, supply or finance, and build against your own numbers.
    Included
    Faster time to proof of value
    Sixty days is enough to see whether Genie moves a number, before you commit budget to anything.
    Genie Kick Start
    $0 Genie Program Timeline

    A defined path with a decision point at the end, not an open-ended pilot.

    Week 1-2
    Scope and semantics

    Pick the business question worth answering. Review existing models and BI patterns, define the KPIs and metric views in Unity Catalog that ground the answers.

    Week 3-4
    Build the Genie space

    Configure the space against your data, add ground-truth question and answer pairs, wire in the surfaces your teams use, and set governance.

    Week 5-8
    Evaluate and decide

    Benchmark accuracy against known answers, put it in front of real users, review adoption and cost, then decide on scale-up with evidence in hand.

    Genie is only as good as the foundation beneath it

    Genie Ontology learns from what your Unity Catalog knows. Governance, semantics and pipeline quality are not preparation for the AI project, they are the AI project. That is the work we have been doing on Databricks for years.

    01

    Early access, before the market

    As a launch partner for Genie and Lakebase, we put new platform capabilities into client stacks while they are still new, often with a zero-cost trial to lower the entry barrier.

    02

    Foundation-first, not demo-first

    Medallion architecture on Delta Lake, quality checks at every layer, and Unity Catalog governing lineage and access from day one. Genie inherits that rigour.

    03

    Domain depth in CPG, retail and life sciences

    We know what incremental lift, depletion, and on-shelf availability mean in practice, so the semantic layer reflects your business rather than a generic template.

    04

    Cost discipline built in

    A FinOps practice that has cut Databricks spend by 17% at a pharma GCC and 42% on optimized workloads elsewhere. AI adoption should not arrive with a surprise bill.

    05

    Adoption is designed, not hoped for

    Enablement, change programs and no-code interfaces for commercial teams. One alcobev client ran 50+ change programs with over 90% adoption.

    06

    Products that extend Genie

    PromoPulse, PricePulse and CapitalPulse sit natively on Databricks, so Genie answers connect to the trade, pricing and finance decisions that follow.

    1,800+
    Data and AI projects delivered
    Silver
    Databricks Brickbuilder partner
    6-40
    Weeks, typical engagement range
    Foundations we have already built on Databricks
    150+ source systems harmonized into one platform

    A global alcobev leader had finance, marketing, HR and supply chain in silos with no standard framework for governance. We migrated the estate to a Databricks harmonized mesh with a Bronze to Platinum quality progression.


    2X
    Faster
    reporting
    3X
    Faster
    insights
    250+
    Man hours
    saved
    17% cost savings with FinOps on Azure Databricks

    A global pharma GCC ran 150,000+ datasets and 40,000+ pipelines with no unified lineage or cost visibility. We stood up a centralized FinOps and governance framework with end-to-end lineage from source to Power BI.


    17%
    Spend
    reduction
    100%
    Cloud Cost
    Visibility
    150K+
    Datasets
    streamlined
    75% lower cloud cost on offer model pipelines

    A global retail GCC ran on legacy Hive data with weak governance and costly, slow pipelines. We migrated to Databricks Unity Catalog, added MLflow versioning and CI/CD, and right-sized clusters to cut cost and runtime.


    75%
    Lower
    Cloud Cost
    40%
    Faster
    Runtime
    90%
    CLV Pipeline
    Savings
    Talk to our Experts