For engineering leaders, data architects, and the people in the C-suite who've run an AI pilot and now need to know what production actually looks like. Covers architecture decisions, governance, and how to sequence implementation against real outcomes – not a polished demo.
Assessment across data maturity, infrastructure, skills, governance, and organisational readiness. Tells you where you actually stand – which is usually different from where you think you stand.
Foundation model selection, vector store choice, orchestration, evaluation, deployment – a decision framework for each layer, with the cost and control trade-offs written out plainly rather than left as an exercise.
A roadmap sequenced by dependency, not by what looks good in a slide deck. Milestones that mean something. Governance checkpoints timed to prevent the failure modes we've actually seen.
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How to evaluate and select foundation models against your actual use case. Includes cost modelling, latency requirements, and an honest look at when fine-tuning is and isn't worth the effort.
RAG patterns, vector store selection, embedding strategy, and chunking decisions. The choices that determine whether your AI system is useful in production or just impressive in a controlled demo.
Tool use patterns, multi-agent architectures, and orchestration framework trade-offs. The failure modes that only show up at scale – not in your local environment or staging slot.
LLM evaluation frameworks, output validation, and grounding techniques. Also the production monitoring approach that gives teams actual confidence in what they've deployed, rather than just optimism.
Token cost modelling, caching strategies, model routing, and AI governance frameworks. Human-in-the-loop design and audit logging requirements across jurisdictions – before someone in Legal asks.
Prompt injection mitigations, output sanitisation, and model access controls. Standard application security applied naively is not enough here. The playbook covers what's specific to generative AI components.
CI/CD for AI systems, model versioning, drift detection, and A/B testing for generative outputs. The operational checklist you work through before anything touches production.
The 90-day roadmap. Dependencies ordered correctly. Governance checkpoints placed where they prevent things rather than just document them. Built around failure modes we've seen, not invented ones.
If it raises a decision worth a second opinion, our AI and data practitioners are easy to find. We won't be pitching you. Just a conversation.
