Use-case discovery
A working session to map ideas against value, feasibility and risk, ending with a prioritised shortlist.
Know what to build, and whether what you built is working.
Hands-on advice from an engineer who ships. I help you choose AI use cases worth doing, pick models and architecture, and review existing LLM applications for quality, cost, latency and security, ending with a prioritised plan your team can act on.
Sound familiar?
You're not sure which AI use cases are worth the investment.
An LLM app is live, but costs are climbing or users don't trust it.
There's no evaluation, so every prompt change is a gamble.
What you get
A typical engagement covers the following, tailored to your stack and goals.
A working session to map ideas against value, feasibility and risk, ending with a prioritised shortlist.
Model, framework and cloud recommendations that fit your data, budget and compliance needs.
A review of answer quality, hallucinations, latency, cost and security, with concrete findings.
Golden datasets, RAGAS and LLM-as-a-judge set up so your team can measure every change.
Token usage, caching, model routing and budgets: where the spend goes and how to cut it.
A clear report with prioritised recommendations, effort estimates and next steps.
Use cases
Toolkit
I pick tools to fit your environment and constraints, not the other way round. Models and vendors stay swappable.
Related experience
From enterprise platforms I've built. Client names are withheld.
FAQ
A written report with findings ranked by impact, the evidence behind each one, and a prioritised plan with effort estimates, walked through together on a call.
Yes. Many audits continue into a build or a retainer, but the report is written so your own team can act on it too.
Tell me what you're trying to build. In a free 30-minute call we'll work out whether AI is the right tool, what it would take, and whether I'm the right person to build it.