Expertise
AI Engineering
Enterprise AI that leaves the demo stage: retrieval-augmented generation on governed cloud platforms, internal AI tools that staff actually use, and AI-assisted analysis that leaders can trust.
The problem
Every board now has an AI mandate, and most organisations already have a pilot that impressed a meeting and then stalled. The hard part is not the model. It is connecting AI to internal documents safely, keeping data inside the right boundary, controlling cost per query, and producing answers that a regulator or an auditor can trace back to a source. Without that engineering, AI stays a slide in the strategy deck instead of a tool that saves time every day.
How I approach it
Start from a decision or a workflow, not from a model. Each use case is framed by who will use it, what they decide with it, and how success will be measured.
Ground answers in your own knowledge. Retrieval-augmented generation connects language models to curated internal sources, so answers cite documents instead of guessing.
Build on the cloud you already govern. Deploying on services such as Azure OpenAI keeps data, identity and network controls inside the organisation’s existing security perimeter.
Engineer for operations. Prompt and retrieval quality, usage, latency and cost are monitored like any other production service, with clear owners and change control.
Keep a human in the loop. For analysis and advisory work, AI drafts and challenges; people decide. Outputs separate fact, inference and recommendation so reviewers can check each claim.
Prove value early, then scale. A focused pilot with real users and a clear baseline shows time saved and answer quality before wider rollout, so expansion is a business decision backed by evidence.
Outcomes
- An AI-powered internal tool with retrieval-augmented generation built on Azure OpenAI at a state-owned enterprise.
- An AI-assisted policy and economic analysis panel that stress-tests proposals from several expert perspectives and produces evidence-traced executive briefs.
- AI delivery that sits on the same Terraform-managed, cost-governed infrastructure as the rest of the platform, not beside it.
Technologies
- Azure OpenAI
- Retrieval-augmented generation (RAG)
- Large language models
- Microsoft Azure
- Terraform
Related case studies
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