Methodology
Your expertise. Amplified by AI.
We teach deterministic-first, AI-enabled engineering: reliable systems built around what you know, with AI where it adds real value.
AI interprets. Rules decide. People approve.
A reference architecture for freight exception handling: the shape of almost every system we teach you to build.
- InCarrier emails, proof-of-delivery scans, TMS events
- AI layerExtract shipment IDs and delay reasons, classify the exception, draft the customer update
- Deterministic coreValidate against the TMS record, apply SLA and escalation rules, check permissions, write the audit log
- OutA dispatcher approves the outbound message
Why deterministic first
Reliable business systems need explicit architecture. Business rules, workflows, permissions, validations, structured knowledge and critical decisions should not be left entirely to probabilistic AI behaviour.
So we start with what must always behave the same way, and then give AI a clear job inside it: understanding language, drafting, classifying, summarizing, suggesting. The result is easier to test, to explain and to improve.
The method, step by step
-
Start from a workflow you know well: where it slows down, where errors creep in, where customers wait. Turn it into requirements with measurable outcomes, so you know what “working” means before anything is built.
What you produceA problem statement, success criteria and the scope you will not touch.
-
Map the workflow as it really runs: the inputs, the states, the decisions and the exceptions people handle without thinking about them.
What you produceA workflow model with every decision written down.
-
Separate deterministic logic — rules, permissions, calculations, state — from the places where probabilistic AI genuinely adds value: reading, classifying, drafting, summarizing.
What you produceA system diagram showing what the rules own and what AI assists with.
-
Use AI-assisted development tools to build a working solution, with validation, safeguards and observable behaviour designed in from the start.
What you produceA working, scoped application or automation.
-
Test against real scenarios and edge cases from your operation. Identify the risks, and recognise when specialist engineering, security or compliance input is needed.
What you produceA test record and a list of known limits.
-
Deploy, measure against the outcomes you defined, and maintain the solution as the operation changes.
What you produceA deployed solution and a documented portfolio project.
What you learn to tell apart
Deterministic operations
Calculations, rules and steps that must give the same answer every time.
Probabilistic reasoning
Where AI's judgment with language and ambiguity is genuinely useful — and where it isn't.
Structured business knowledge
Your expertise written down as data, rules and decisions a system can use.
System integrations
How a solution exchanges data with the tools a business already runs.
Data validation
Checking what goes in and what comes out before anyone relies on it.
Human oversight
Where a person must review, approve or decide.
The design depends on the problem
There is no universal architecture. Different requirements, risks and expected behaviour lead to different designs — choosing well is part of the skill.
When AI should not decide
- Amounts, eligibility and compliance rules
- Permissions and access to data
- Anything that must be audited exactly
When AI helps
- Reading and drafting language
- Sorting and summarizing unstructured inputs
- Suggesting options for a person to choose
Part of the method is knowing when a project needs professional engineering, security, legal, regulatory or domain-specific support.
You already have the knowledge. Build with it.
Start with a free live webinar. Bring a problem from your own work.