AI to Production
An AI feature that works in a demo and fails on a real user. We harden it: prompts, cost, fallbacks, data, and a deploy you can repeat. Not another wrapper around a chat box.
What you get
An AI feature that survives contact
- A keep or rewrite call
- What in the prototype can stay. What will break on day two.
- Fails you can see
- Bad model output is caught. It does not ship as a confident lie.
- Cost under control
- Limits, logging, a model choice that matches the job.
- In the real workflow
- Connected to the tools and the step a person already does.
What we harden
Six gaps between demo and production
The demo already answers. Production is everything around that answer.
- 01
Read the prototype
You get
A verdict on the current AI build. No surprise rewrite.
Where prompts live, what data it sees, what happens when the model is wrong or slow. You get a keep / fix / replace list before we rebuild anything.
- 02
Prompts and outputs
You get
Prompts you can change. Output that fails closed on facts.
Prompts out of the UI and out of scattered files. Output checked against what must be exact. If the job cannot tolerate invention, the system says so instead of guessing.
- 03
Fallbacks
You get
A failure path. Not a spinner that never ends.
Timeout, empty response, a model that is down. A path that does not white-screen. A human step where the automation must stop.
- 04
Cost and limits
You get
A cost ceiling and a log. No unbounded calls.
Which model, what it costs per run, a cap so a loop cannot burn the budget overnight. Logging so you can see what was sent and what came back.
- 05
Data and access
You get
A boundary on data. Keys not in the client.
What the model is allowed to see. User data not pasted into a prompt by accident. Auth around the feature, not a public endpoint with a key in the front end.
- 06
Deploy and handover
You get
A repeatable deploy and a map of what remains fragile.
It ships without a person pasting files. You get how to run it, what is still fragile, and access. Support can take it from here, or your own team can.
A working prompt is not a product
Vibe coding gets you a demo fast. It does not get you limits, a fallback, or a way to know the model invented a fact. We close that gap. If the base cannot hold it, we say replace, not polish.
This is the AI-specific path. A non-AI MVP that needs auth and a deploy is MVP to Production. The free audit is the start if you are not sure which leak you have.
Related: MVP to Production, AI Automation, SaaS Support
Next step
Around AI production
Questions
Before you take an AI demo live
Is this different from MVP to Production?
Yes. That one is the product floor: auth, data, deploy. This one is the AI layer: prompts, cost, fallbacks, and output you can trust.
Can you work with something I generated?
Yes. We read it first and say what can stay. A full rewrite only if the base cannot hold users.
Will it stop inventing facts?
Where facts must be exact, we fail closed and hand off. We do not promise a model that never errs. We promise you see the error.
Do I need a new model or vendor?
Usually no. We work with what you have, add limits, and change model only if the job requires it.
What do you need from me?
Access to the prototype, what the AI must never get wrong, and what “live” means for a user.
Start
Get the AI build read
Keep, fix, or replace. Before a demo meets a real user.