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OwnIntelligence
    Proof of conceptSovereign enterprise AI infrastructure

    Your Business
    Intelligence.
    Fully Owned.

    Build enterprise AI on your terms. Combine private organizational knowledge with customizable open-weight models, transparent governance, and controlled deployment.

    Enter the Model Lab
    Explore the Architecture

    Thesis

    Most enterprises rent their intelligence. Their knowledge flows into systems they cannot inspect, tune, or move. Own Intelligence turns that around — your data, your models, your rules, running where you decide.

    02From rented AI to owned intelligence

    Stop sending your knowledge out. Bring the intelligence in.

    Your organization boundaryYour intelligenceVendor-hosted modelOpen-weight modelShared API endpointYour private endpointData sent to vendorData stays in your vaultOpaque model updatesVersioned adapters & evals

    Five dimensions of ownership

    State: Owned

    Model weightsRented ·Closed weights you can call but never inspect or keep.Owned ·Open weights you download, inspect, version and run — under the model's license.
    Model hostingRented ·Runs on the provider's shared infrastructure.Owned ·Runs on infrastructure you operate: private cloud, your cluster, or on-prem.
    Enterprise dataRented ·Prompts and documents leave your boundary for every request.Owned ·Knowledge is indexed and retrieved inside your boundary, under your access rules.
    CustomizationRented ·Limited to prompts, or vendor-run tuning you can't take with you.Owned ·Retrieval, instructions and adapter weights are artifacts you own and can move.
    Operational controlRented ·Deprecations, pricing and behavior change on someone else's schedule.Owned ·You decide when models change, what is logged, and who approves releases.

    Open-weight is not the same as owning the base model. The model's intellectual property stays with its publisher and every family ships its own license terms. What you own is your data, your customizations, your deployment and your operational decisions.

    03The intelligence architecture

    Eight layers. One system you control.

    Scroll through the stack. The core re-forms at each layer, from distributed data to a governed deployment. Select any layer to see what it does.
    1. Connectors read in place with scoped service identitiesClassification travels with every dataset: Public → RestrictedNothing is copied to a third-party model providerSee it in the Data Vault
    2. PII detection and redaction transforms in the pipelineContent hashing for lineage and change detectionIngestion runs inside your boundarySee it in the Data Vault
    3. Chunking, embedding and hybrid keyword + vector searchReranking orders passages by relevanceEvery answer carries citations back to sourceSee it in the Model Studio
    4. Swap models without rewriting applicationsSize the model to the hardware and latency budgetEach family's license terms still applySee it in the Model Registry
    5. Retrieval and instructions change behavior without changing weightsLoRA adapters are small, separate, portable artifactsEvaluation gates on groundedness, safety and PII leakageSee it in the Model Studio
    6. Role-based access on datasets and modelsApproval workflows for restricted data and productionImmutable audit trail and end-to-end lineageSee it in the Governance Center
    7. Private endpoints, no public ingress by defaultSigned artifacts promoted across environmentsPolicy engine and audit sink deployed alongside inferenceSee it in the Deployment Control
    8. Policy copilots, research assistants, support agentsShared retrieval and evaluation across appsHuman review routes for low-confidence answersSee it in the Playground
    04Model freedom

    Choose the foundation. Change it later.

    Build on open-weight model families and keep the freedom to switch as the field moves. Your retrieval, evaluations and governance stay in place when the model underneath changes.

    Selection workspace

    2/3
    MistralLlama

    Your knowledge · evals · policies — unchanged

    ModelContextGroundedness illus.
    Mistral Small 3 (24B)32K82
    Llama 3.1 8B Instruct128K78

    Parameters, context and license reflect public model cards at authoring time; verify before use. Scores and latency are illustrative demo values, not benchmarks.

    05Governance by design

    Policy isn't a document. It's part of the system.

    Every model, dataset and deployment passes through the same governance layer. Watch a simulated policy event travel through an approval workflow, then change the data classification and run it again.

    Approval workflow

    Simulated
    1. 1Deploy request
    2. 2Classification
    3. 3Policy engine
    4. 4Steward review
    5. 5Deployment
    Scroll into view to start the policy event…

    audit.logtail -f

      Open the Governance Center
      06Enterprise use cases

      One core. Many intelligent applications.

      Illustrative scenarios showing how the platform could be applied. These are not client deployments.

      Stop Renting Intelligence. Start Building Your Own.

      Launch the AI Model Lab
      Take the guided build
      0

      open-weight models in the demo catalog

      0

      Model Lab modules

      0

      subsystems in the intelligence graph

      0

      bytes sent to external AI providers in demo mode

      Figures describe this proof of concept, not customer results.