Enterprise AI Architecture

AI Without Architecture Is Expensive Chaos

Before committing further capital — or scaling beyond early pilots — establish architectural clarity.

A structured architecture sprint for organisations where AI touches live production systems, delivering integration risk mapping, cost exposure modelling, and a clear proceed / pause / re-architect decision.

Get Architectural Clarity Download the 12-Point AI Readiness Diagnostic
20+
Years in Systems Architecture
1M+
Platform Users Served
2–4wk
Sprint Duration
The Problem

Most AI initiatives fail before the model is chosen.

01

Bolt-on AI collapses under real load

CRM integrations that work in demos but fracture at scale. The architecture wasn't designed for the data volume, latency requirements, or failure modes that production demands.

02

Retrieval without data clarity becomes misinformation

RAG implementations built on unstructured, unverified data layers produce confident answers from unreliable sources. The retrieval works. The data underneath does not.

03

Hidden cost curves derail budgets and operations

Token costs, retraining cycles, embedding storage, and governance overhead compound faster than initial projections. The architecture assessment should precede the vendor call.

Recurring Patterns

Across organisations evaluating AI integration, certain structural themes repeat.

01

AI pilots launched before system topology was mapped

Retrieval layers deployed on unstable or conflicting data sources. Cost projections based solely on model pricing rather than infrastructure growth.

02

Governance implications discovered after deployment

Sequencing driven by enthusiasm rather than architectural readiness. Compliance requirements surfaced too late to course-correct affordably.

03

The model is rarely the root issue

The surrounding architecture determines viability. Without structural clarity, even well-chosen models fail in production environments.

What Changes

After the Sprint

Before
  • Unclear whether AI is viable in your current architecture
  • Unknown integration risks and failure modes
  • Cost projections based on vendor estimates
  • Roadmap driven by enthusiasm, not evidence
After
  • You know whether to proceed, pause, or re-architect — before committing further capital
  • Integration risks are mapped and sequenced
  • Cost exposure is modelled against your infrastructure
  • Deployment is structured in staged, defensible phases
  • Executive decisions are grounded in architectural clarity

The objective is decision confidence, not documentation.

You will know whether to stabilise, scale, or structurally reconfigure — before additional exposure compounds.

The Sprint

Architecture Before AI — 2–4 Week Sprint

A structured risk-elimination product. Not advisory. Not assessment. A decision-enabling deliverable.

Suitable for CTOs, CIOs, and executive teams requiring defensible decisions.

Suitable for both pre-deployment validation and post-pilot architectural review.

Duration
2–4 Weeks
Investment
$18k–$25k
Engagement Model
Limited engagements to maintain depth and rigour
01

System Topology Audit

Complete mapping of your existing system landscape.

  • All core platforms
  • Data ingress/egress points
  • Latency & dependency mapping
02

Data Integrity & Retrieval Readiness

Evaluate data fitness for AI consumption.

  • Source reliability scoring
  • Structured vs unstructured classification
  • Retrieval readiness assessment
03

Failure Mode & Cost Simulation

Realistic projections against your infrastructure.

  • Projected usage and cost modelling
  • Long-term data storage growth modelling
  • Governance overhead forecast
04

Architectural Stress Test

Pressure-test integration assumptions.

  • Load assumptions
  • Model switching resilience
  • Vendor dependency risk assessment
05

Deployment Sequencing Blueprint

Staged execution plan with clear decision gates.

  • Stage 0: Pre-AI prerequisites
  • Stage 1: Low-risk pilot
  • Stage 2: Scaled integration
  • Stage 3: Optimisation & monitoring
06

Executive Decision Brief

Board-ready clarity on next steps.

  • Proceed / Pause / Re-Architect determination
  • Capital allocation implications
  • Risk-adjusted deployment sequencing
  • Structural remediation roadmap where required

All outputs are implementation-ready — suitable for internal teams or external vendors. Ongoing advisory support is optional.

Architectural Inflection

When Early Momentum Meets Architectural Friction

01

Latency and reliability instability under real usage

In many organisations, AI initiatives begin with contained pilots. Friction often appears only when scale, integration depth, or operational exposure increases.

02

Conflicting data and escalating costs

Conflicting data surfaced through retrieval layers. Escalating operational cost beyond early projections. Governance and compliance surface expansion.

03

Structural clarity at inflection points

The Sprint provides structural clarity at these inflection points — identifying whether to proceed, pause, or re-architect before further capital or complexity accumulates.

Qualification

This is for you if

  • You are accountable for live production systems, not experiments
  • AI integration will touch operational data, workflows, or customer-facing systems
  • Capital allocation requires defensible architectural clarity
  • You need a structured, defensible architecture assessment — not presentation theatre

This is not for you if

  • You're exploring AI as a proof-of-concept experiment
  • You're seeking model selection advice without systems review
  • You want a vendor comparison
  • Your systems are greenfield or pre-revenue

Typical clients are established organisations with live production systems and meaningful operational complexity.

How It Works

Three steps to architectural clarity.

01

Architecture Call

We assess your current systems, strategic goals, and AI ambitions. You leave with an initial read on risk and opportunity.

02

Sprint Execution

Deep-dive into your systems landscape. We map architecture, data layers, integration paths, and failure modes.

03

Roadmap Delivery

You receive a practical, prioritised execution plan with cost models, risk assessment, and a clear path forward.

Lee Powell
Oxford MSc
TOGAF Certified
CBA & Deutsche Bank
1M+ User Platform Founder
About

Lee Powell

An architect-led technologist with 20+ years integrating complex systems and launching global platforms. Former Solution & Enterprise Architect inside tier-1 banks, founder of a software platform that served over one million users, and TOGAF certified.

Lee helps organisations bring AI into real systems without destabilising core infrastructure. The work starts with architecture — not vendor selection, not model benchmarking, not proof-of-concept theatre. Architecture.

Free Diagnostic

The 12-Point AI Readiness Diagnostic

A self-assessment that tells you whether your architecture is ready for AI — or whether you're building on assumptions. It diagnoses surface-level readiness. The Sprint addresses what lies underneath.

Download the Diagnostic
12 Categories Scored 1–5
01 Data Structure Maturity
02 Data Lineage Visibility
03 API Surface Stability
04 Observability Coverage
05 Latency Tolerance
06 Compliance Constraints
07 Vendor Lock-In Risk
08 Cost Exposure Awareness
09 Internal Capability
10 Executive Alignment
11 Security Posture
12 Integration Complexity
Score below 38? You are not architecture-ready for AI.
Why This Exists

The Sprint exists to create clarity before commitment.

It is designed to surface architectural constraints early — not to extend consulting engagements.

You should leave knowing whether AI is viable in your current structure.

Get Started

Get Clarity Before You Commit Capital

If AI integration will affect production systems, begin with architectural review.

Initial Fit Call

15 minutes. Purpose: determine suitability and scope. No obligation.

Request Architecture Sprint

All fields required. We respond within 48 hours.