Real World AI Design
The Method

Applied AI Architecture — a practical operating model for production AI.

A reference architecture isn't just technology. It's services that work as a system. The companies actually shipping AI today have built capability across five connected domains. Most failed pilots ignored at least three of them.

DATA → INTELLIGENCE → DECISION → ACTION → FEEDBACK

The lifecycle every AI system has to support.

Treat AI as a complete system, not a model in isolation. Every stage either makes the next one easier or harder. Skip a stage and the system breaks — usually somewhere it's hard to see until it's expensive.

01

Data

Sources, quality, governance

02

Intelligence

Models, prompts, retrieval

03

Decision

Rules, workflows, guardrails

04

Action

Apps, automation, integration

05

Feedback

Monitoring, learning, improvement

What goes wrong without the full lifecycle

A model is built and demoed (Intelligence), but no one closed the loop on data quality (Data), so it works on the demo set and degrades in production. There's no integration into the workflow (Action), so users don't adopt it. There's no monitoring (Feedback), so when accuracy drifts, no one notices for months.

What working systems look like

Data flows through governed pipelines into a model that returns a structured decision. The decision triggers an action inside the system the team already uses. Outcomes are logged and fed back into evaluation. The system gets measurably better over time, not worse.

Readiness scoring

Five domains. One scorecard.

Before designing an AI architecture, score readiness across five domains. The scores tell you where to start, what to fix first, and which use cases are realistic right now versus which need foundational work.

DOMAIN 1

Data

Is the data usable, available, structured, and governed?

DOMAIN 2

Systems

Can current tools support APIs, automation, and integration?

DOMAIN 3

Use Cases

Are business problems measurable and worth solving?

DOMAIN 4

Organization

Are stakeholders, owners, and adoption paths clear?

DOMAIN 5

Governance

Are privacy, safety, explainability, and controls defined?

A note on what these scores actually do.
Scores aren't grades. They're triage. A 30/100 in Data isn't a failure — it tells us the first 6 weeks of work is data engineering, not model selection. A 90/100 in Use Cases means we can move directly to design. The point is to start where the work actually is.
Engagement flow

From assessment to architecture to roadmap.

Every engagement follows the same sequence. The depth varies by tier. The order doesn't.

STEP 01

Discovery + Readiness

Stakeholder interviews, system review, data inventory, risk scan.

STEP 02

Use Case Prioritization

Rank opportunities by impact, feasibility, risk, and time-to-value.

STEP 03

Architecture Design

Design data flow, AI services, integrations, monitoring, and governance.

STEP 04

Roadmap + Proposal

Milestones, implementation options, budget bands, and next-step proposal.

See how your organization scores.

The free AI Readiness Assessment runs the five-domain scorecard against your situation and produces a personalized report. About 8 minutes. No sales call attached.