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Docker for AI

NeuralSeek is Docker for AI.

Every component a custom AI application needs — the model, your data, knowledge, actions, guardrails and orchestration — in one container you run anywhere.

Request a demoSee how the layers fit together

Why Docker for AI

What Docker did for apps, NeuralSeek does for AI.

Docker ended "it works on my machine" by shipping the app with everything it needs. AI applications have the same problem, one layer up.

  • The problem

    Docker, for apps

    "It works on my machine" — the code ran where it was written and nowhere else.

    NeuralSeek, for AI

    It works with this model, this index and this prompt — move it, or change the model, and the wiring breaks.

  • One package

    Docker, for apps

    One image carries the code, the runtime, the libraries and the config.

    NeuralSeek, for AI

    One container carries the model, your data connections, knowledge, actions, guardrails and orchestration.

  • Change a part, not the whole

    Docker, for apps

    Update one layer of the image without rebuilding the app.

    NeuralSeek, for AI

    Swap the model per step without rewiring the agent — it is a layer inside, not the foundation under it.

  • Same result, anywhere

    Docker, for apps

    The same image runs on a laptop, a server or a cloud.

    NeuralSeek, for AI

    The same container runs on AWS, Azure, IBM Cloud or your own Kubernetes or OpenShift cluster.

What is in the container

Every component, and the page that explains it.

Nothing here is a sidecar you operate or a bolt-on you buy later. Each component has its own page; this one says they ship together.

One NeuralSeek container

  • The model

    Chosen per step and swapped without a rebuild, because it is a layer inside the container rather than the foundation under it.

    See agent workflows

  • Your data

    Reached where it already lives — your databases, document stores and search infrastructure — rather than copied into ours.

    See the connectors

  • Knowledge

    Answers grounded in your own corpus, scored for how well the sources support them, and cited back to the passage.

    See semantic retrieval

  • Actions

    The nodes that act in your systems — a ticket, a record, a message, a file — through the same connectors that read from them.

    See the connectors

  • Guardrails

    Screening, redaction and grounding gates in the request path, enforced at runtime rather than written into a prompt.

    See the guardrails

  • Orchestration

    Multi-step agents with a model per step, parallel branches and a human gate where you put one — authored in the same container they run in.

    See agent workflows

The record of what every one of these did — the audit trail, the run replay and the usage figures — is the governance layer, which has its own page under Platform.

In the product

One flow, every component.

A short capture from the agent editor. Nothing here is a diagram; every box is a node that runs.

Personal data is removed, profanity filtered, the model called and its answer scored, then two warehouses queried — one canvas, one agent, one run record. That is what a container with every component inside looks like from the editor.

PII removed, profanity filtered

Guardrails
Redaction, a profanity filter, the model call, a semantic score and warehouse queries in one agent.

Run it anywhere

Where it runs is a decision you keep.

Because everything is in the one container, where it runs is a purchasing and jurisdiction decision rather than an engineering one.

Nothing about the container changes between those two pages — only who administers it, and where.

See it running inside your own boundary.

Talk to a NeuralSeek expert about how it fits your stack, where your data has to live, and the governance your auditors already expect.

Request a demo