AI Infrastructure & LLMOps

LLMOps

A language model in production is a dependency you do not control, priced per use, whose behaviour changes without your release cycle. We build the deployment, evaluation, monitoring and cost control that makes that manageable.

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The business problem

Quality drifts and nobody is watching

Traditional monitoring answers whether the service responded, not whether the answer was any good. So quality degrades invisibly: a provider updates a model, a prompt change has a side effect, retrieval goes stale. The first signal is usually a customer complaint. Cost fails the same way, arriving as a surprise invoice with no attribution to the feature that caused it.

What we do

Instrument quality and cost as first-class signals

We put an evaluation suite in the deployment pipeline, so a prompt or model change is scored before it ships and continuously afterwards. Prompts are versioned like code. Traces capture inputs, retrieved context, tool calls and outputs, so any answer can be reconstructed. Cost is attributed per feature and per team, with routing that sends routine traffic to cheaper models and escalates only what needs it.

LLMOps

Capabilities

LLM Deployment
LLM Monitoring
Evaluation Pipelines
Prompt Management
Observability
Routing
Cost Monitoring

Common use cases

Add evaluation gates so a prompt change cannot ship without being scored.
Attribute inference spend to the feature and team causing it.
Detect quality regression after a provider updates a model.
Cut cost by routing routine traffic to a smaller model with escalation.

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 treat evaluation as the release gate

Most teams evaluate once before launch and then rely on complaints. Continuous evaluation against a labelled set is the difference between knowing quality and hoping. It is also the only way a model upgrade becomes routine rather than frightening.

Selected clients

Frequently asked questions

How do you evaluate something with no single right answer?

With criteria rather than exact matching: was it grounded in the retrieved source, did it answer what was asked, did it avoid asserting anything unsupported. Scored by a model against a rubric, with human review on a sample to keep the scorer honest.

How much does the observability cost?

Meaningfully less than the spend it lets you avoid. Cost attribution alone typically finds a double-digit percentage of waste in the first month, usually from prompts sending more context than needed.

Can you retrofit this onto systems already live?

Yes, and that is the common case. Instrumentation goes in first, which usually reveals the quality and cost picture nobody had, then evaluation and routing follow.

Which tools do you use?

OpenTelemetry for tracing so the data stays yours, with your existing observability stack as the destination. We avoid platforms that hold your traces hostage.

Related capabilities

AI Infrastructure & LLMOps

AI Infrastructure

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

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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

Managed AI Services

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

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Discuss Your AI Initiative

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

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