AI delivery
You built it with AI. Now make it real.
You used AI to write a script, wire up a workflow, or knock together a prototype that actually solves a problem. Now you need it hosted, stable, and usable by more than just you. We take what you have built and ship it as a production service.
The gap between "it works" and "it runs"
It works on your machine.
The script runs. The chatbot answers. The pipeline processes the data. But it lives on your laptop, in a notebook, or in a one-off resource with no auth, no logging, and no way to share it safely.
Going from prototype to product is a different skill.
AI tools make it fast to write something that works. Running it in production, with authentication, monitoring, cost controls, and a team who can support it, is a different problem. That gap is where things stall.
You should own the outcome, not depend on the original author.
Whether you or someone on your team built it, a production service needs documentation, alerting, and a clear path to update it when the underlying model or API changes.
What we do
Productionising AI-built tools.
We take working prototypes and make them into proper hosted services on the Microsoft Azure stack.
Code review and hardening
AI-generated code works, but often skips error handling, secrets management, and input validation. We review what you have, fix the gaps, and make it something you can maintain.
Hosting and infrastructure
Containerised deployment on Azure: App Service, Container Apps, or Functions depending on the workload. Proper resource groups, naming conventions, and access control from day one.
Authentication and access control
Entra ID integration so your team logs in with their existing Microsoft accounts. Role-based access so the right people can use it, and the wrong ones cannot.
Monitoring and alerting
Application Insights, cost alerts, and structured logging. You will know when it breaks before your users do, and you will be able to see what the AI calls are costing.
API or web interface
A clean API endpoint or web UI so others on your team can use it without touching the code. We build what fits the workflow, not what is most technically interesting.
Documentation and handover
Architecture diagram, runbook, and update guide. When the model version changes or you need to swap the underlying API, someone on your team can do it.
The process
How a productionisation engagement works
Show us what you built
Share the code, a demo, or a description of what it does and where it lives. We do not need it to be clean. We need to understand what you are starting with.
We assess what it needs
A short discovery engagement. We review the code, identify gaps, and map out what hosting, auth, and monitoring would look like. You get a clear picture of the work involved before anything is agreed.
Fixed-price proposal
Based on the assessment, you receive a scoped proposal. No open-ended engagements.
We build and deploy
We take your prototype, harden the code, and deploy it to Azure. Iterative delivery, so you see progress before it goes live.
Handover to your team
Documentation, architecture diagram, and a walkthrough. Your team can support it, update it, and extend it without us.
FAQ
Common questions
What counts as "something I built with AI"?
Anything that works. A Python script you wrote with Cursor, a Power Automate flow, a chatbot wired up with the OpenAI API, a data pipeline you put together in a weekend. If it solves a real problem and you want other people to be able to use it, we can productionise it.
Does the code need to be in good shape?
No. We start from wherever you are. Reviewing and hardening AI-generated code is part of what we do. That usually means filling in the error handling, secrets management, and edge cases that were skipped in the original build.
What if it uses the OpenAI API rather than Azure?
We can either migrate it to Azure OpenAI Service for data residency and governance, or keep the existing provider and productionise the infrastructure around it. We will recommend what makes sense for the use case.
What if I built it myself and my team does not know it exists yet?
That is the most common starting point. You built something that saved you hours, and now you want your whole team to have it. We will help you get it to a state where you can share it without it being your personal thing that might break.
Can you help with the AI design too, or just the infrastructure?
Both. We can review the prompt design, add RAG over your internal documents, swap in a more appropriate model, or add structured outputs. The infrastructure work and the AI improvement often happen in the same engagement.
Got something that works and needs to run properly?
Show us what you have built. We will tell you what it would take to make it production-ready.