Delivery knowledge
Technical decisions, written so they can be challenged.
First-party working guidance for leaders, product owners and engineering teams deciding how to scope, evaluate, integrate, release and operate AI-enabled software. Every long-form guide names its author, review date, sources and evidence boundary.
Technical guide · Technology leaders, delivery owners and teams with a blocked release or unstable workflow.
Urgent software rescue: from symptom to a safe change
A technical decision guide for stabilising a blocked release, critical defect or failing workflow without confusing urgency with uncontrolled change.Technical guide · Product leaders and sponsors deciding whether an urgent AI MVP is feasible.
From a seven-day AI MVP to a controlled pilot
How to define a one-week AI MVP and turn its evidence into a controlled pilot rather than a compressed product roadmap.Technical guide · Product, engineering, data and risk teams releasing AI-enabled workflows.
Production AI evaluation: evidence before release
A technical approach to evaluating AI-enabled workflows using representative cases, defined failure modes and decision-ready release records.Technical guide · Operational leaders, product owners and teams designing AI-assisted decisions or actions.
Human oversight that can intervene in AI workflows
How to design human review, override and escalation so that oversight is an operational control rather than a passive disclaimer.Technical guide · Engineering leads, architects and product owners integrating AI capabilities into existing systems.
API and integration architecture for AI-enabled software
A decision guide for connecting AI-enabled workflows to operational systems without making the model a hidden, over-privileged integration layer.Technical guide · Product, operations and engineering teams responsible for an AI-enabled workflow after release.
Operating AI in production: observability, exceptions and change
How to run an AI-enabled workflow as an observable operational service rather than a one-off model demonstration.Technical guide · Technology and procurement leaders choosing an AI model or platform provider for a specific workload.
Choose an AI model provider by workload, not brand
A structured decision framework for comparing model-provider options against the actual product, data, control and operating requirements.Technical guide · Engineering, product and operations teams preparing an AI-enabled production release.
Production release and rollback for AI-enabled software
A release-management guide for shipping AI-enabled workflow changes with acceptance evidence, operational controls and a realistic recovery route.Urgent delivery · 8 min read
What makes a seven-day MVP realistic?
The decisions, constraints and evidence that keep an urgent build focused.AI transformation · 9 min read
Start with a workflow, not an AI feature list
A practical way to select a pilot that teams can actually adopt.Responsible AI · 9 min read
The operating questions behind responsible AI
A plain-language view of purpose, oversight, data and accountability.A practical next step
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