AI Architecture & Platform Engineering

An AI system in production is mostly not a model. It is retrieval, orchestration, authorisation, caching, evaluation, fallbacks, logging and spend management. Get that layer wrong and every use case you build on top of it inherits the problem.

We design AI platforms as shared infrastructure: build it once, then let each new use case land in weeks rather than quarters. This matters most for organisations that expect to run five or fifty AI systems, not one.

Platforms we typically deliver this on

DatabricksMicrosoft AzureAWSGoogle CloudSnowflakeOracle

All platforms and partners

What we do

Scope of the service

Reference architecture

A documented target-state architecture for your environment covering ingestion, storage, retrieval, orchestration, serving and observability.

Model hosting and routing

Selection and deployment across hosted APIs, cloud-native model services and self-hosted open-weight models, with routing by cost and sensitivity.

Retrieval and vector infrastructure

Chunking strategy, embedding pipelines, index design, hybrid search and permission-aware retrieval so users only ever see what they may see.

Identity and access integration

Integration with Entra ID, Okta or on-premises directories so AI systems inherit existing entitlements instead of inventing new ones.

Evaluation and observability

Automated evaluation suites, regression gates in CI, tracing, and dashboards for quality, latency and spend.

Data residency and isolation

Architectures that keep regulated data in-region, including Canadian-only deployment patterns and tenant isolation for multi-agency platforms.

Use cases

Where this is used

Representative engagements, described at the level our clients permit. Sector and shape are accurate; identifying detail is withheld.

Public sector

A shared AI platform for multiple agencies

Rather than each department procuring separately, we designed a shared platform with per-tenant isolation, central governance and chargeback, so new departments onboard in weeks against pre-approved controls.

Outcome — New use cases onboarded without repeating the security review each time.

Healthcare

Permission-aware retrieval over clinical documentation

Staff needed to search across policy, procedure and clinical guidance without any chance of surfacing material outside their role. We built retrieval that filters at query time against the source system's own entitlements.

Outcome — No parallel permission model to maintain, and no risk of drift between the two.

Financial services

Controlling inference spend at scale

An early rollout was consuming budget unpredictably. We introduced model routing, response caching, prompt compression and per-team quotas with alerting.

Outcome — Run costs made predictable and attributable to the teams generating them.

Questions we are asked

Before you get in touch

Do you work on our cloud or recommend a new one?

We build on what you already run. Most of our work is on Azure, AWS, Google Cloud or Databricks, and we design for portability where the commercial risk justifies it.

Can models run entirely inside our environment?

Yes. Where residency or sensitivity requires it we deploy open-weight models within your tenancy or on-premises, and design the surrounding platform accordingly.

How do you keep AI systems from drifting out of compliance?

Controls are enforced in the platform rather than documented as guidance: retrieval respects source entitlements, evaluations gate deployment, and every interaction is logged for audit.

Related services

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The plumbing that decides whether AI works at scale