A practical guide to modernising legacy systems: when to act, six strategies compared, what AI coding agents change, cloud and data migration, a phased roadmap, ROI and risks.
A CTO’s guide to connecting AI models and agents to existing systems without rewriting them: five integration patterns, data readiness, security, costs and a step-by-step plan.
A practical guide to implementing generative AI: which use cases pay off, how to choose models, when to use RAG, fine-tuning or agents, and how to run it safely.
A practical roadmap for taking a predictive model or LLM agent from idea to production: use-case scoring, a 90-day pilot, evals, cost control and honest ROI.
How to decide which features a custom software project builds first: the main frameworks compared, a step-by-step MVP scoping process, and how to re-prioritise after launch.
A practical guide to adding AI to iOS and Android apps: features that pay off, on-device vs cloud models, Siri and Gemini actions, consent rules, a 90-day plan and ROI metrics.
A phase-by-phase playbook for moving applications and data to AWS, Azure or Google Cloud, from the 7 Rs and landing zones to cutover, cost control and AI readiness.
A practical guide to moving a monolith to cloud-native microservices: when it is worth it, the strangler-fig method, a six-month roadmap, costs and pitfalls.
A practical MLOps guide for growing companies: how to version, deploy, monitor and govern ML and LLM models, which tools to use, and a 90-day plan to get there.
Developing a mobile or web app can be an exciting venture—but it can quickly turn into a stressful, expensive experience if timelines
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