AI Infrastructure & LLMOps

AI Architecture

Without a reference architecture, every team independently picks a model, a vector store, an orchestration approach and a way of handling secrets. We define the shared architecture that makes the second and fifth AI system cheaper than the first.

Discuss Your AI Initiative Consulting

The business problem

Nothing compounds

The first AI system is expensive because everything is new. The fifth should be cheap and usually is not, because each team solved the same problems differently. Now there are five ways of calling a model, five approaches to evaluation, five secret stores and no ability to move traffic between providers. The cost is paid twice: once in duplicated build, and again when something needs to change everywhere at once.

What we do

Define the shared layer and its boundaries

We design the layer that should be common, model access, retrieval, orchestration, evaluation, observability and secrets, and are equally explicit about what should stay with the application, because over-centralising creates a bottleneck. The architecture is written to keep provider choice reversible, so a model decision does not become an architectural commitment. We validate it against two or three real use cases rather than shipping a diagram.

AI Architecture

Capabilities

Enterprise AI Architecture
LLM Architecture
RAG Architecture
Agent Architecture
AI Platform Architecture
Cloud AI Architecture
Hybrid AI
Private AI Architecture

Common use cases

Establish a reference architecture before a portfolio of AI projects starts in parallel.
Consolidate several teams' divergent implementations onto shared infrastructure.
Design for a jurisdiction or isolation requirement that rules out the default cloud path.
Review an architecture that is proving expensive to 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 validate architectures by building on them

An architecture that has never carried a real workload is a hypothesis. We test the design against actual use cases during the engagement, which surfaces the wrong assumptions while they are still cheap to correct. It also keeps the document honest about what is genuinely shared and what only looked shared on a diagram.

Selected clients

Frequently asked questions

Should we build a platform or let teams choose?

Somewhere between, and the line matters more than the choice. Centralise model access, evaluation, observability and secrets, because those benefit from consistency. Leave application logic and user experience with the teams, because centralising those creates a queue.

How do we avoid vendor lock-in?

By routing model calls through a gateway with a stable internal interface, so provider choice is configuration. Complete portability is not achievable because model behaviour differs, but the cost of switching can be made a week rather than a quarter.

Do we need a dedicated vector database?

Often not. pgvector in existing PostgreSQL serves many enterprise workloads without new infrastructure to operate. A dedicated store earns its place at scale or for specific features, not by default.

How long does this take?

Six to ten weeks including validation against real use cases. Shorter engagements produce a document; the validation is what makes it an architecture.

Related capabilities

AI Data & Models

AI Data Strategy

Make enterprise knowledge usable by AI: readiness, ownership, quality and retrieval architecture.

Learn more

AI Security

AI Security Consulting

Threat-model AI systems and design the architecture, controls and boundaries that contain their risk.

Learn more

AI Strategy & Transformation

AI Strategy

Decide where AI belongs in your business, what it is worth, and in what order to build it.

Learn more

Discuss Your AI Initiative

Design the reference architecture your AI systems share: models, retrieval, orchestration, data and controls.

Talk to an AI Expert Start an AI Assessment