Important decisions.
Experienced support.

Architecture assessment & review

Understand where risk and delay come from.

For systems becoming difficult to change, costly to operate or hard to trust. We examine architecture alongside delivery practices, operational evidence, ownership and business priorities.

What you leave with

• A clear picture of the system and critical dependencies.
• Prioritised findings with evidence and business consequences.
• Options for improvement, including costs and trade-offs.
• A practical sequence of actions with accountable owners.

What we can measureChange lead time, recurring incidents, recovery time and unresolved risks.

Evolutionary architecture

Make change manageable as you grow.

For systems that need to evolve while continuing to serve customers. Define useful boundaries, introduce checks for important rules and plan incremental changes around delivery needs.

What you leave with

• Principles tied to the qualities your system needs.
• A staged migration or improvement plan.
• Checks for dependencies, performance and resilience.
• Clear points for reviewing assumptions and changing direction.

What we can measureIndependently releasable changes, cross-team dependencies, migration progress and change failure rate.

AI-enabled architecture

Establish where AI earns its place.

For organisations assessing AI capabilities or moving a prototype towards operational use. Examine the task, its information needs and the consequences of a poor result.

What you leave with

• A use-case assessment with a clear baseline.
• Model and system options, including simpler alternatives.
• An evaluation plan covering quality, failure and cost.
• Boundaries for data access, human review and fallback behaviour.

What we can measureAccepted task completion, unsupported claims, latency, review effort and cost per accepted result.

Programme-scale alignment

Connect decisions to the work that matters.

For organisations where multiple teams depend on shared systems and scarce expertise. Start with demand, then map the capabilities, decisions and dependencies required to deliver it.

What you leave with

• A shared view of demand and architectural dependencies.
• Clear decision responsibilities across teams.
• Work sequenced around capacity and priorities.
• Governance proportionate to the decisions being made.

What we can measureBlocked work, decision turnaround, dependency age and delivery predictability.

Modern engineering practices

Build the conditions for reliable delivery.

For teams slowed by fragile releases, inconsistent environments or limited feedback. Connect architectural choices with testing, automation, observability and operational ownership.

What you leave with

• A review of the path from code change to production.
• Targeted improvements to feedback and release confidence.
• Practical reliability and recovery measures.
• Coaching and working examples your team can maintain.

What we can measureDeployment frequency, lead time, change failure rate and recovery time.

AI governance & safe adoption

Clarify responsibility before increasing autonomy.

For organisations introducing AI tools or agents into consequential workflows. Define permitted actions, evidence requirements and human responsibilities around the actual task.

What you leave with

• A map of AI uses, owners and material risks.
• Rules for information access and tool permissions.
• Evaluation, approval and escalation criteria.
• A plan for reassessing behaviour as models and workflows change.

What we can measureUnauthorised actions in testing, control failures, escalation effectiveness and unresolved high-priority findings.