AI Infrastructure & LLMOps

Private AI

For some workloads a hosted API is not an option, whatever the contract says. We deploy models inside your own boundary: your cloud tenancy, your data centre, your network.

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The business problem

The blocker is rarely technical

The obstacle is usually a legal, regulatory or contractual constraint on where data may be processed, and no amount of vendor assurance resolves it. Teams then either stall, or proceed with a data classification exercise that quietly reclassifies the problem away. Neither is a good outcome, and the second one surfaces later at a worse moment.

What we do

Run the model where the data already is

We deploy open-weight models into your environment, sized against the workload rather than the largest available, and build the serving, scaling and observability around them. Quality is measured against your actual use cases, so the trade against a frontier model is quantified rather than assumed. Where only part of the workload is sensitive, we design the split so the constrained path is genuinely isolated.

Private AI

Capabilities

Private Cloud AI
On-Premise AI
Isolated AI Environments
Private LLM Deployment

Common use cases

Process regulated data where a hosted API is contractually or legally unavailable.
Operate in an environment with no reliable external connectivity.
Meet a customer requirement that their data never reaches a third-party model provider.
Make inference cost predictable at high, steady volume.

How we deliver

Assess

Establish the constraints: residency, latency, spend and what already runs.
Architect

Design gateway, routing, caching and failover so provider choice stays reversible.
Instrument

Observability, evaluation and cost attribution wired in before traffic arrives.
Operate

Run it against agreed service levels, with capacity and spend reviewed on a cycle.

Technology

Kubernetes
Terraform
vLLM
Ollama
LiteLLM
OpenTelemetry
Prometheus
Grafana
AWS
Microsoft Azure

Security & governance

Where data may be processed is a configuration, not an assumption: models can run in your own cloud tenancy or on your own hardware where residency or isolation requires it. Traffic through the gateway is authenticated, attributed to a team and logged, which is what makes both the audit trail and the cost model possible.

Independently audited against

Engagement models

AI Project

We take responsibility for designing and delivering a defined AI solution.

Dedicated AI Team

Long-term dedicated engineering capacity built around your stack and delivery model.

Managed AI

We operate, monitor and continuously improve production AI systems.

Why TeamExtension.ai

We quantify what you give up

Self-hosted models are behind frontier models on hard reasoning, and pretending otherwise sets a project up to disappoint. We measure the gap on your use cases so the decision is made with a number rather than a preference.

Selected clients

Frequently asked questions

How much capability do we lose?

It depends entirely on the task. For extraction, classification and grounded question answering, often very little. For complex multi-step reasoning, more. We measure it on your cases rather than quoting benchmarks.

What hardware is needed?

Less than usually assumed. Many enterprise workloads run comfortably on a single modern GPU per instance, because the constraint is concurrency rather than model size. We size against measured load.

Who operates it?

Your platform team, with us building it and handing over, or us under a managed arrangement. Self-hosting is an operational commitment and should be entered deliberately.

Can we mix hosted and private?

Yes, and it is common. Sensitive workloads run privately while the rest uses hosted models, with the gateway enforcing which path a given request may take.

Related capabilities

AI Infrastructure & LLMOps