AI that runs entirely inside your walls.
For the environments that cannot send a single token to an external API — defence, government, banking, healthcare and critical infrastructure. Air-gapped, privacy-preserving, fine-tuned models and agents, governed by the same control plane, running on hardware you own.
The hardest enterprise AI problem is the environment it is not allowed to reach.
The most valuable and most regulated work happens where sending data to a hosted model API is simply not an option — for reasons of law, classification, contract or risk. That is exactly where most AI platforms stop. Bixie is built to run there: the whole platform, fully governed, with no assumption that anything can call home.
No route out, and nothing that needs one.
Models, gateway, agents, knowledge and the control plane all run inside your network, with no dependency on an external API. Nothing has to reach the internet for the platform to work.
Deploy on your own GPUs — a private data centre, a sovereign cloud region, or a disconnected enclave. The same platform, whether it has a route to the internet or not.
Egress is denied by default and every outbound call is accountable. What can leave the boundary is a policy decision you own, not an assumption baked into a vendor SDK.
Your data, your weights, your boundary.
Prompts, documents, embeddings, memory and model outputs stay inside infrastructure you operate. There is no readable waypoint on someone else’s servers.
Adapt models on your own data in place. Training data, weights and adapters remain yours and stay inside the enclave — they are never pooled, shared or used to train anyone else’s model.
Sensitive-data detection, PII protection and data-loss prevention run on every request, so even inside the walls a model only ever sees what policy allows it to see.
Because everything runs where you put it, data residency stops being a contractual promise and becomes a property of the deployment.
Models that know your organisation — not a generic one.
Adapt open-weight models on your own data inside the enclave, register them alongside commercial models in one governed catalogue, and route each task to the model that fits — proven against your scenarios before it serves a single request.
Fine-tune open-weight models on your own documents, tickets, code and transactions so they speak your organisation’s language — terminology, formats and policies included — instead of a generic one.
Register open-weight, commercial and your own fine-tuned models in one governed catalogue. Route each task to the right model by capability, cost, latency and data sensitivity — never bound to a single provider.
Every model and adapter is measured against your real scenarios — accuracy, grounding, tool-call correctness and policy compliance — before it is allowed to serve production traffic.
Promote, roll back and retire model versions like any other release. Who deployed which model, when, and how it scored is a matter of record.
Where the stakes make sovereignty non-negotiable.
The whole platform comes with you — including the guardrails.
Going air-gapped does not mean giving up control. Identity, policy, approvals, agent identity and audit all run inside the boundary, so a disconnected deployment is as governed as a connected one.
Identity, permissions, policy, approvals and audit — enforced in the path of every request, with or without an internet connection.
Bixie Attest gives every agent its own short-lived identity so each action is bound to a named agent and owner. The trust bundle runs inside your boundary too.
Every model call, retrieval, tool invocation and approval is recorded locally — the evidence a regulator or reviewer asks for, held on your side of the line.
However isolated you need to be.
Bring enterprise AI to the places it could not go before.
Tell us about your environment — the constraints, the classification, the hardware — and we will show you exactly how Bixie runs inside it.