Deployment & economics
Most of the cost and effort sits between a working demo and production. Confusing a successful PoC with a finished product is the most common AI budgeting mistake.
In plain terms
A proof of concept tests whether an idea can work, a pilot tests it with a small group of real users, and production is the fully supported system everyone relies on.
A proof of concept asks can this work at all, on a small set of real examples, with engineers nearby. A pilot asks will people use it on real work, for a few weeks, with a success line written down. Production means it runs without you watching, with an owner, a bill, a failure plan, and a way to turn it off.
Most AI projects die between pilot and production, because the demo had no owner and no number that would justify the running cost. Name that number before the pilot. If you cannot say what success looks like in a count, a time, or a cost, you are still in a demo, whatever the slide says.
Think of it this way: A PoC is a sketch. A pilot is a limited print run. Production is the full press. Each stage costs more, moves slower, and delivers more confidence than the last.
A logistics company spends three weeks on a route optimization PoC. It works. They run a 90-day pilot with one region. Validated. Full production build takes eight months and the serious engineering budget.
A team proves a bot can answer thirty policy questions. That is the proof of concept. Twenty staff use it for a month and the team counts how often the answer is accepted. That is the pilot. They then assign an owner, a spend cap, and an on-call, and open it to the department. That is production. Skipping the middle step is how a demo becomes an outage.
Every AI investment should move through these stages deliberately. A PoC answers 'can this work.' A pilot answers 'does this work in our context.' Production is the decision to operate and support it indefinitely. Do not treat a successful PoC as proof the production system is built. The gap between a working demo and a maintained, scalable, secure product is where most AI projects underestimate both cost and time. Production typically costs several times more than the PoC.
RaftLabs prices the running cost before the build, so a feature people like does not become a loss. You get a number for a busy month, not only a demo. The related work on our side is AI PoC development.
This sits with the other deployment & economics terms on the glossary. What you pay to run AI, and the choices that change the bill. Worth reading next: API, Open vs Closed Models, and Inference Cost.
Work with us
Tell us what's not working in your business. We'll find the real problem and tell you exactly what it would take to fix it.