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AI Infrastructure & LLMOps

Managed AI Services

AI systems drift in ways conventional software does not: providers change models, content goes stale, cost moves with usage. We operate them, monitor quality rather than only uptime, and improve them continuously.

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The business problem

It was delivered, and then it was nobody's

Projects end and systems continue. Without a named owner and real monitoring, quality erodes quietly: a provider update shifts behaviour, an index stops refreshing, costs creep. Nothing alerts, because uptime monitoring says everything is fine. The system is quietly abandoned long before anyone formally decommissions it.

What we do

Run it against agreed service levels

We monitor answer quality continuously against an evaluation suite, not just availability. Cost is tracked and attributed, with routing tuned as usage patterns change. Model and prompt changes go through evaluation before release. Incidents have a response path, and improvement work is planned rather than reactive. Everything runs in your environment, and you can take it back at any point.

Managed AI Services

Capabilities

  • AI System Monitoring

  • Agent Monitoring

  • Model Monitoring

  • Continuous Evaluation

  • Incident Management

  • Managed LLMOps

Common use cases

Common use cases

  • Operate a production AI system where you have no team to own it.
  • Add quality and cost monitoring to systems that only have uptime alerting.
  • Cover the period between delivery and an internal team being ready.
  • Keep evaluation and routing current as models and pricing change.

How we deliver

How we deliver

  1. Assess

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

  2. Architect

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

  3. Instrument

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

  4. Operate

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

Technology

Technology

  • Kubernetes
  • Terraform
  • vLLM
  • Ollama
  • LiteLLM
  • OpenTelemetry
  • Prometheus
  • Grafana
  • AWS
  • Microsoft Azure

Security & governance

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

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 operate what we build

Knowing a system will be handed back to us shapes how it is built: instrumented, documented and recoverable. It also means the improvement work is informed by having watched it fail.

Selected clients

Frequently asked questions

Frequently asked questions

What is actually monitored?
Answer quality against an evaluation suite, retrieval health, latency, error rates, cost per feature and drift in usage patterns. Uptime is the least interesting signal.
Can you operate something you did not build?
Yes, after an assessment. We usually need to add instrumentation and an evaluation suite first, because most systems arrive without them.
Are we locked in?
No. Everything runs in your environment, in your repositories, documented. Handover to your own team is a planned transition rather than a renegotiation.
How does this work commercially?
A monthly arrangement against agreed service levels and a defined scope of systems, with improvement work planned in cycles rather than billed as incidents.

Related capabilities

Related capabilities

AI Infrastructure & LLMOps

LLMOps

Deploy, monitor, evaluate and cost-control language models once they are carrying real traffic.

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AI Infrastructure & LLMOps

MLOps

Lifecycle management for machine learning: deployment, monitoring, retraining and CI/CD.

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AI Infrastructure & LLMOps

AI Infrastructure

Gateways, routing, caching and failover so model choice stays a configuration, not an architecture.

Learn more

Discuss Your AI Initiative

We operate, monitor and continuously improve the AI systems already carrying your production load.

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