Capabilities

Three disciplines.
One architecture.

AI only earns trust when the data beneath it is sound and the controls around it are real. We run all three as one practice, so nothing is bolted on afterwards.

AI & Intelligent Systems

Production-grade AI, from copilots and agents to decision systems. Built on your data, evaluated continuously, and designed to be explained.

How we judge success

Every system ships with an evaluation set, a cost model and a plain-language explanation of what it does and does not do. If we cannot measure it, we do not call it done.

What we build

  • Copilots and assistants grounded in your documents and systems
  • Voice and chat agents that take real actions, not just answer
  • Decision and classification models with human-in-the-loop review
  • Evaluation harnesses, guardrails and monitoring that run in CI
  • Document intelligence: extraction, summarisation, triage

Typical engagements

  • AI opportunity assessment (2–4 weeks)
  • Agent pilot to production in one quarter
  • Model evaluation and red-team review
  • LLM platform and gateway build

Data & Cloud Architecture

The platforms that make AI possible: modern data foundations, cloud architecture and integration that hold up under enterprise load.

Why it comes first

Most failed AI programmes fail on data: access, quality or lineage. We fix the foundation in the same engagement so the model work is not built on sand.

What we build

  • Lakehouse and warehouse foundations on Azure, AWS or GCP
  • Data movement between on-prem, cloud and third-party providers
  • Streaming and event-driven integration
  • Self-service data products with lineage and ownership
  • Landing zones, identity and network design for regulated workloads

Typical engagements

  • Target-state architecture and roadmap
  • Data platform build or modernisation
  • Cloud migration for a regulated workload
  • Integration and API layer for AI consumption

Security & Governance

Security, privacy and model governance designed in from day one, so regulators, boards and customers can trust what you ship.

Governance as code

Controls that live in documents get skipped. Ours run in the pipeline and the platform, so the evidence a regulator asks for already exists.

What we build

  • AI governance frameworks aligned to ISO 42001 and the Privacy Act
  • Policy-as-code: access, data classification and retention enforced in the pipeline
  • Threat modelling for LLM systems: prompt injection, data leakage, tool misuse
  • Audit trails, model registries and evaluation evidence for review
  • Operating models and controls your own team can run

Typical engagements

  • AI risk and readiness review
  • Security architecture for an AI platform
  • Governance operating model and controls design
  • Board and risk-committee briefing

Not sure where to start?

A short assessment maps your data, systems and risk posture and ranks the opportunities. Most clients begin there.

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