Discipline 01 · Architecture

Design it once, on paper, before it costs money.

What a solution design looks like.

Representative specimen — the shape of a solution design, not a client system Fig. 01

Layer 01 Sources Systems of record, documents, streams
Layer 02 Data & retrieval Pipelines, index, versioning
Layer 03 Orchestration Routing, state, tool access
Layer 04 Model access Provider abstraction, cost, residency
Layer 05 Production Serving, observability, rollback

Guardrails & evaluation

Human oversight

For CTOs and heads of architecture with one shot at building it right
Vendor-neutral: we do not resell, and we do not bid for the build
Costed, with the running bill made explicit before you commit
Handover, not dependency — your team owns what we hand over

4 engagements.

Every engagement is fixed in scope before it starts, and what it costs is agreed in the scoping call and confirmed in writing. Set that against the build it protects — and against paying for that build a second time. The comparison is the whole argument for hiring an architect.

Architecture · Engagement 01

AI Discovery & Scoping

Decide what to build — and whether to build it at all.

Most failed AI programmes were mis-scoped before a line of code was written. Discovery replaces the enthusiasm with a defined problem, tested feasibility, costed options and a delivery approach you can put in front of a board.

Scope

Problem definition and the decision the system is meant to support
Success criteria, and what would count as evidence of them
Data availability, quality, rights and access constraints
Technical feasibility assessment against the stated criteria
Build, buy and hybrid options with their trade-offs made explicit
Indicative cost envelope and run-rate drivers
Delivery approach, sequencing and the capability required to execute it
Early risk, governance and regulatory flags

Deliverables · 6

Discovery report with the problem statement and success criteria
Feasibility assessment and evidence base
Costed options analysis with a recommendation
Delivery roadmap and resourcing shape
Risk and governance flag register
Executive read-out session

Comes up when · An executive mandate to "do something with AI" without a defined problem.

Architecture · Engagement 02

AI Solution Design

An architecture your engineers can build from on Monday.

A design that stops at a box diagram is a slide, not an architecture. Solution Design produces the component design, data flows, interface contracts, evaluation strategy, control points and non-functional requirements needed to build the system and to defend it in review.

Scope

Target solution architecture and component design
Data architecture, flows, lineage and retention
Model, retrieval and orchestration strategy with the reasoning recorded
Interface and integration contracts
Evaluation strategy — what is measured, when, and against what threshold
Non-functional requirements: performance, cost, resilience, scalability
Security architecture and control points
Human oversight and intervention design
Observability, monitoring and incident paths
Build sequencing and architectural decision records

Deliverables · 7

Solution design document, implementation-ready
Architecture and data-flow diagrams
Architecture decision records with the rejected options recorded
Evaluation and acceptance criteria
Non-functional requirement set
Control points mapped for governance sign-off
Design walkthrough with the delivery team

Comes up when · A funded initiative with no architecture behind it.

Architecture · Engagement 03

AI Architecture Review

An independent read on the architecture you already have.

Internal teams cannot mark their own homework, and vendors have an interest in the answer. A structured, independent review tells you where the architecture holds, where it will fail under load, cost or scrutiny, and what to do about it in priority order.

Scope

Architecture assessment against the stated intent and requirements
Data flow, dependency and coupling analysis
Model and retrieval design review, including evaluation adequacy
Scalability, performance and cost-behaviour assessment
Security, access and data-exposure review
Resilience, failure modes and fallback behaviour
Operability: monitoring, drift detection, incident response
Governance and control-point gaps
Prioritised findings with remediation options

Deliverables · 5

Architecture review report with severity-rated findings
Risk-weighted remediation backlog
Target-state recommendation and transition options
Evidence pack for architecture or assurance boards
Findings walkthrough with the owning team

Comes up when · A system approaching production with no independent assessment.

Architecture · Engagement 04

Enterprise AI Platform Architecture

Stop rebuilding the same AI plumbing in every team.

When each team solves identity, evaluation, guardrails, observability and model access on its own, the organisation pays for it several times and governs none of it consistently. A platform architecture defines the shared foundation, the reference patterns teams build against, and the sequence for getting there from where you are.

Scope

Current-state assessment across teams, platforms and vendors
Target reference architecture and the capabilities it must provide
Model access, routing and vendor-abstraction strategy
Shared evaluation, guardrail and safety tooling
Data and knowledge-access architecture
Identity, entitlement and tenancy model
Observability, cost attribution and FinOps design
Reference patterns and paved paths for delivery teams
Governance and stage-gate integration
Transition roadmap with sequencing, dependencies and decision points

Deliverables · 6

Enterprise AI reference architecture
Capability model and current-state gap assessment
Reference patterns and paved-path definitions
Vendor and build/buy position per capability
Transition roadmap with phased decision gates
Executive and architecture-board briefing pack

Comes up when · Multiple teams independently standing up their own AI stacks.

Architecture

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