Why teams choose self-hosted AI infrastructure
APIs are fast to adopt; cost and opacity hurt at scale. We architect stacks where you retain ownership and can measure SLAs.
What we deliver
Serving and data architecture
Clear interfaces from data to models with explicit latency budgets.
Kubernetes and releases
GitOps-style deployments with rollbacks and reproducible environments.
Operations and observability
Metrics and alerts so failures surface before users do.
Engineering standards in production
What we hold constant across deployments
Versioned artefacts
What runs in production is reviewable end-to-end.
Least privilege
Minimal access for models and pipelines.
Transfer
Runbooks and pairing so your team operates confidently.
Where this fits
Sensitive data posture
When flows and hosting must stay explainable.
Growing usage
When load increases and transparency is missing.
Vendor independence
When open interfaces matter strategically.
FAQ
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Architecture-only?
Yes—scope is agreed explicitly per engagement.
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Which models?
Depends on licence, latency, and policy—we evaluate together.
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GPU operations?
Yes, including capacity planning and monitoring patterns.
Contact form
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