Local AI for sensitive data.
Local open-weight models process sensitive data on your hardware. For explicitly approved non-sensitive tasks, you can connect commercial frontier models through secure interfaces. Access rules keep sensitive data local and control which external interfaces may be used.
Sensitive data stays in house.
Access rules determine which requests may use external models.
Local inference
With local inference, your own hardware processes prompts and documents inside your network.
Data sovereignty
Local models process sensitive data without sending it to third parties. Access rules keep sensitive content away from external APIs.
Hybrid when it helps
You can route explicitly approved, non-sensitive tasks to an external API.
Open weights
We use inspectable, swappable open-weight models. The platform stays independent of any single provider.
What inference needs.
A usable AI platform needs a gateway, knowledge integration, access control and operations alongside inference. We build these components as one stack.
Local inference
We serve open-weight models of different sizes efficiently on GPUs.
Model gateway
A central gateway handles routing, quotas, key management and cost tracking for every model.
Embeddings & vector search
We make your content quickly searchable with local semantic search.
RAG & knowledge
The answers draw on your documents and include source references.
Chat interface
Your team accesses the models through a self-hosted interface. The interface and its data remain under your control.
SSO & access control
Users sign in through your identity system. You assign roles and permissions by team and use case.
Observability & cost
We track utilization, latency and consumption so you can plan capacity and costs.
Workflow automation
We integrate models into automated processes and your existing systems.
Platform build and operations.
We build the platform as versioned code and run it day-to-day. We can also hand it over cleanly to your team.
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Needs & sizing
We determine suitable hardware and model sizes from your use cases and privacy requirements.
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Platform deployment
We deploy the full stack as Infrastructure as Code, so it can be reproduced at any time.
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Model selection & tuning
We select models for your tasks and weigh quality against resource use.
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Knowledge integration
We index your documents and data sources and make them available through retrieval.
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Access & SSO
We connect the platform to your identity system, configure roles and secure the endpoints.
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Operations & monitoring
We handle updates, scaling, backups and observability. Alternatively, we train your team to operate the platform.
Sensitive data stays on your hardware, and local tasks run there.
You can swap models without tying the platform to one provider.
Requests to external models pass through the gateway and follow the same access rules.
Usage and costs remain visible, making monthly spending easier to forecast.
Local AI on your infrastructure.
Tell us briefly about your use case. We'll propose hardware, models and a platform build.