How to move an AI prototype into production

01Why the usual way fails

A prototype skips the parts that make a system safe to depend on. It often calls a public API with whatever data it gets, has no monitoring when answers go wrong, and lives in one person's notebook or script. Moving it to production means choosing a model setup that fits your cost, latency and compliance limits, adding access controls, and writing down how it works so others can operate it. Teams that try to do all of this while shipping features usually end up with a fragile system only one engineer understands. Hiring a full platform team for one AI project is slow and expensive, and many teams do not yet know which skills they need.

02How AI & Automation Consulting helps

Quinn Favo is an AI infrastructure consultant who helps teams make this step. Work starts with a free call about goals and constraints, then usually an assessment of your current setup before anything is built. From there the engagement builds the production layer, whether that means frontier APIs, a private model on your own infrastructure or a mix of both. It ends with documentation and runbooks so your own engineers can run the system afterwards.

03Step by step

1. Book the free 30-minute call to go over goals, constraints and fit.
2. Run an audit of your current automation flows, prompts, tool access and data boundaries.
3. Pick the model strategy based on cost, compliance, latency and capability.
4. Build one real production use case with the right model layer and one business integration.
5. Hand off docs, runbooks and infrastructure patterns to your engineers.

04Tips

• Start with one use case that real users depend on, not the whole roadmap. A small system in production teaches you more than a big one in a demo.
• Decide early which data may leave your network. That single choice decides whether you can use public APIs, need private inference, or need a mix of both.
• When answers go wrong, check retrieval first. Many failures come from missing permissions, old content or a pipeline nobody can inspect, not from the model.
• Write runbooks while you build, not after. If only one person knows how to restart or roll back the system, it is not ready for production.
• Measure cost per request from the first week in production. A setup that is cheap in a demo can become expensive at real traffic, and early numbers show whether a private model or an API fits better.

05Questions