AI Infrastructure & LLMOps

AI Infrastructure

Applications should not each hold their own provider credentials, retry logic and cost exposure. We build the gateway layer that centralises model access, so provider choice stays a configuration rather than an architectural commitment.

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The business problem

Every application is its own integration

Without a shared layer, each application authenticates separately, handles failure differently and spends without attribution. Changing provider means touching every codebase. An outage takes down whatever happened to be pointed at it. And because nobody can see aggregate usage, cost management is retrospective.

What we do

Gateway, routing, caching, failover

We build a gateway that owns credentials, applies per-team quotas, caches what is cacheable, and fails over when a provider degrades. Routing sends traffic to the appropriate model by task rather than by whichever was configured first. Every call is traced and attributed, which is what makes both the audit trail and the cost model possible. Applications talk to a stable internal interface and stop caring which provider is behind it.

AI Infrastructure

Capabilities

AI Model Gateways
Model Routing
AI Observability
Inference Infrastructure
Caching
Failover
Multi-Provider AI Infrastructure

Common use cases

Consolidate provider access for a growing number of AI applications.
Survive a provider outage without an application outage.
Attribute inference spend by team and enforce quotas.
Make switching or adding a model provider a configuration change.

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 build for reversibility

Provider decisions made today will look wrong within eighteen months, because the market moves faster than procurement cycles. Designing so that decision stays cheap to revisit is worth more than getting it right the first time.

Selected clients

Frequently asked questions

Does a gateway add latency?

A few milliseconds, against model latency measured in hundreds. Caching typically makes the net effect negative, because repeated calls stop reaching the provider at all.

Build or buy?

Depends on requirements. Several capable open-source gateways cover most needs and we deploy those where they fit. We build when residency, identity integration or routing logic makes the off-the-shelf option awkward.

How does failover work if models behave differently?

Failover targets are chosen and evaluated in advance, so the fallback is known to be acceptable for that task rather than merely available. Silent failover to an untested model is worse than a clear error.

Can this enforce policy?

Yes. It is the natural place for quotas, per-team limits, content controls and logging, because everything passes through it. That is much of the reason to have one.

Related capabilities

AI Infrastructure & LLMOps