Take your AI prototype to production.
A prototype only has to work once, for you, with the inputs you expected. Production has to work for every user, with the inputs you didn't expect, at a cost that still makes sense when usage grows. That gap is where most AI products stall. I've closed it on my own products, including voice agents that hold real phone interviews with job candidates, and I'll close it on yours.
AI prototype to production
Sounds like you if
- The demo impresses people, but the answers change every time you run it
- It's slow enough that users notice, and you're not sure where the time goes
- Every prompt or model change is a gamble, because nothing tells you what got worse
- Your AI bill is growing faster than your revenue
- It needs to handle real customer data, and you're not sure how it should
Tools on the bench
- Python
- FastAPI
- LLM APIs
- RAG
- Voice AI
- AI agents
- PostgreSQL
- AWS
- Azure
How I’d handle it
The job, step by step.
- 01
Measure it
Before changing anything, I build an evaluation set from real examples, so we can see how good it is today and prove that each change makes it better instead of guessing.
- 02
Harden it
Retrieval that finds the right context, structured outputs, guardrails, retries and fallbacks for when a model provider has a bad day, and careful handling of personal data.
- 03
Run it
Tracing for every AI call, budgets for cost and latency, alerts when quality drops, and a deploy setup that lets you ship prompt and model changes without holding your breath.
What you walk away with.
- 01An evaluation suite built from your real use cases, run on every change
- 02Production architecture for your AI features: retrieval, agents, queues and caching where they earn their place
- 03Latency and cost brought down, with the numbers before and after
- 04Monitoring and alerts for quality, errors and spend
- 05Documentation and a handover your team can run with
Shipped, not just pitched.
Products of mine where the same work is running for real users.
Questions people ask first.
Which models and providers do you work with?
Whichever fits the job. I'm not tied to one provider, and part of the work is often moving the parts that don't need the biggest model onto a faster, cheaper one.
Someone else built our prototype. Is that a problem?
No, that's the usual case. If most of it was written by an AI tool, the AI-generated code page covers the same work from a messier starting point.
Do you build the whole product or only the AI part?
Either. I'm a product engineer as much as an AI one, so I can own the backend, the frontend and the AI features, or slot into your team on just the AI layer.
What about data privacy and GDPR?
It comes up in the first week, not the last. We go through what data the model sees, where it's sent and stored, and what your users agreed to, and design around that instead of bolting it on.
Where are you based?
Northampton, UK. I work remotely with teams anywhere in the world.
Other jobs I take on