Most AI demos die in the gap between a working prototype and something a business depends on every day. Crossing that gap is about evaluation, guardrails, observability and integration — not a better model. Here's the checklist we use.
A prototype only has to work once, on a good day, in front of a friendly audience. Production has to work every time, on messy inputs, for people who will stop trusting it after a single bad answer. That's the whole difference — and it's why so many impressive demos quietly disappear.
Here's the checklist we run before we call anything "shipped."
1. Evaluation you can run on every change
If you can't measure whether a change made the system better or worse, you're not engineering — you're guessing. Before we build, we assemble a set of real examples with known-good answers, and we score every change against it.
2. Guardrails for the cases you can't allow
Decide up front what the system must never do, and enforce it in code, not in a prompt. Input validation, output checks, and a confidence threshold below which a human is always in the loop.
3. Observability so trust is earned
Every decision the system makes should be logged, attributable and reviewable. When someone asks "why did it do that?", you need an answer. This is what turns a black box into a tool a team will depend on.
4. Integration into the real workflow
The best AI feature is one your team never has to leave their existing tools to use. We wire into Slack, the CRM, the ERP — wherever the work already happens.
The one-line test
If you'd be comfortable letting it run for a week without watching it, it's production-ready. If you wouldn't, you know exactly what to fix next.
Building something and stuck at the demo-to-production gap? Book a call — it's the gap we cross most often.
Frequently asked questions
Because a demo only has to work once, in front of a friendly audience. Production has to work every time, on messy real-world inputs, for people who'll stop trusting it after one bad answer. The gap is reliability and integration, not model quality.
It means the system has evaluation you can run on every change, guardrails for the inputs and outputs you can't allow, observability so every decision is logged and auditable, and clean integration into the tools and data your team already uses.