Dataplanedataplane
    Enterprise Agents

    Give your enterprise agents context they can trust

    Agents are only reliable when they understand your business and its data. Dataplane turns scattered, ambiguous source data into a clean, governed layer your agents can query with confidence.

    The Problem

    Your business has an ontology. So does the LLM. Their disagreements are your errors.

    Every business runs on an implicit model of its world, what a 'customer' is, when an order is 'shipped', which records are the same entity. The LLM brings its own generic version, and where they diverge, you get errors. We make your model explicit so agents reason on your terms.

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    Your model is implicit. Definitions and relationships live in people's heads, not in the raw columns an agent reads.

    !

    The LLM assumes a generic one. With no model of your business, it falls back on a default that quietly contradicts how you actually operate.

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    Disagreement becomes silent error. When your ontology and the model's diverge, you get hallucinations and wrong answers that hurt the business.

    Dataplane Approach

    From raw data to an agent-ready context layer.

    01

    Model the ontology

    Map raw data to a governed ontology that captures your business concepts, entities, and how they relate.

    model · entities · relationships
    02

    Expose via harness

    Build tools in the agent's harness that let it query the ontology directly, so it reasons on your model instead of guessing.

    tools · query · ground
    03

    Capture & refine

    Use graph access to the ontology to surface missing connections, then refine the model from real agent feedback.

    graph · feedback · refine
    Built on:Data OntologyMaster DataData QualityData Lineage

    Outcomes

    Where You Feel It

    A shared ontology makes agents better, cheaper, and faster.

    Hallucinations

    Grounding agents in your ontology leaves far less room to guess.

    Token cost

    Inject only the specific context an agent needs, not everything just in case.

    Latency

    Smaller, targeted context means shorter prompts and faster responses.

    Time to deploy

    Reuse one ontology across every agent instead of rebuilding context each time.

    Benchmark your agents with an ontology.

    We'll model a slice of your domain and run your agents both ways, so you can see the lift in accuracy, cost, and latency for yourself.