Intent Semantic Physical Technical
    The Enterprise Data Stack

    Put trusted data in the hands of every employee

    Dataplane is the AI-native platform that connects business meaning to enterprise data—powering better decisions by humans and AI alike.

    The semantic layer

    There is a semantic layer beneath every enterprise. We make it explicit

    Every system you deploy (AI, human, or otherwise) either inherits a semantic layer or invents its own. Making it explicit is no longer just good hygiene. It's the difference between technology that compounds your advantage or creates headaches.

    Compiles down
    Intent

    Your competitive advantage. The questions you ask of data, and the insights that drive the business.

    Semantic — entities & rulesDataplane layer

    The shared language underneath every question. Entities, relationships, and the rules that resolve them, defined once and used everywhere. This is ontology.

    Physical — schema mappingDataplane layer

    How those entities bind to the columns and tables you actually have, and re-bind when the schema moves.

    The missing middle
    Technical

    SQL, Python, dbt models, pipelines. The execution. Fast to write, fast to break when meaning is implicit.

    The missing middle

    Two layers most stacks treat as second class.

    Between the intent at the top and the code at the bottom sit the entities and rules that define your business, and the mapping that binds them to the data you actually have.

    It is where a “customer” becomes a precise definition, where two records are judged the same thing, where meaning holds steady even as schemas shift beneath it.

    Made explicit, the missing middle is your ontology: the single model the rest of your stack reads from.

    Ontology as a Hub

    Ontologies coordinate data activities and unlock use cases

    Every data activity implicitly decides what your data means. Every capability you enable implicitly reads those same definitions back. Dataplane meets you where you are and models only the parts your goal actually needs, treating ontologies as an accelerator rather than an academic exercise.

    Data Activities

    feed the ontology

    Data QualityMaster DataData EnrichmentData Lineage

    ...

    Your Ontology

    one customer-owned model of what your data means

    A North Star for Data Efforts

    One definition of what your data means gives every activity a shared target, so quality, mastering, and enrichment pull in the same direction instead of resolving meaning over and over.

    A Compounding Model

    Every activity writes back to the ontology. Each project leaves it richer than it found it, so the next one starts further ahead.

    An Accelerator for Capabilities

    Agents, forecasts, and analytics read meaning straight from the model instead of rebuilding it, so new capabilities ship faster and behave consistently.

    Outcome Driven

    We put context strategies head to head. Ontology wins on every axis.

    27%
    80%
    99%

    System Prompting

    Classic Knowledge Base

    Ontology

    Agent answer accuracy

    The lead is widest on the questions that look trivial to a person but break agents: synonyms, mismatched grain, rules that live in someone's head. Exactly the meaning an ontology makes explicit.

    0%

    lower cost

    0%

    faster responses

    fewer database queries per answer

    Across a fleet of agents, that gap is the difference between a pilot and production.

    4

    weeks to value

    PE-owned industrial manufacturer

    Product data too fragmented to support an ERP modernization. We profiled, standardized, and enriched tens of thousands of SKUs, then stood up monitoring to hold quality. Hundreds of manual hours avoided, and a catalog clean enough to power Amazon-like search.

    Integrations

    Works with your existing data stack

    Dataplane connects directly with warehouses, orchestration tools, BI platforms, and operational systems—strengthening reliability without replacing what you already use.

    Databricks
    BigQuery
    Redshift
    S3
    dbt
    Airflow
    Tableau
    Looker
    Power BI
    SAP
    Oracle
    Databricks
    BigQuery
    Redshift
    S3
    dbt
    Airflow
    Tableau
    Looker
    Power BI
    SAP
    Oracle

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