Every demo works

A demo has one user, who knows exactly what to say. The input is clean and the network is fast. If something goes wrong, you click "run" again. A business has hundreds of users who say things you never expected, on bad phone lines, at the worst possible moment, and nobody clicks "run again" for them. The distance between those two worlds is where most AI projects quietly die. It's also the entire job of a forward deployed engineer. Here are the six gaps I run into on almost every project, with real examples of how each was closed.

1. Clean input vs. real input

In the demo: a nicely typed question. In production: 8 kHz phone audio, background noise and misheard words. In a live call translator I built, transcripts arrive full of small errors. The fix wasn't a better microphone. It was asking the translation model to repair obvious transcription errors as part of its job, using the recent conversation as context. Close the gap: test with the worst real input you can find, not the best.

2. Instant vs. "is anyone there?"

In the demo: nobody notices a two-second pause. In production: on a phone call, two seconds of silence sounds like a dropped line. In a voice agent, a single Google Calendar update took about 2.19 seconds. We couldn't speed up Google, so we hid the wait with pre-recorded filler phrases and preloaded calendar data at the start of the call. Close the gap: measure every step, then remove what you can and hide what you can't.

3. Always answers vs. knows when to stop

In the demo: the AI answers every question beautifully. In production: it answers questions your data never covered, just as beautifully, and wrongly. A handbook assistant I built uses a 0.25 similarity threshold. If nothing in the handbook matches well enough, it says so instead of guessing. Close the gap: decide what "not confident enough" means, and enforce it in code.

4. Reads data vs. changes data

In the demo: the agent books the meeting. Applause. In production: the agent books the wrong meeting for a real customer. Every tool that changes something should refuse to run without explicit confirmation, enforced in the tool's code, not just requested in the prompt. Close the gap: list every side effect, and gate each one.

5. The API is up vs. the API is down

In the demo: every service responds. In production: a provider has an outage on a Tuesday afternoon. An outreach tool I built falls back to a template email when the model is unavailable, so the team's work doesn't stop because a single API failed. Close the gap: for every external call, write down what happens when it fails.

6. Launch day vs. every day after

In the demo: the project ends when it works once. In production: that's when it starts. Real users find the paths you didn't design. On the voice agent, a caller saying "I need help rescheduling" and then "yes" once dropped straight out of the workflow. The fix was a better intent example, found only by reading real transcripts. You won't find these fixes in a planning meeting. You find them in the logs, after launch. Close the gap: plan for weeks of iteration after launch, not just a handover.

The short version

Gap	Demo	Production	Fix
Input	Clean text	Noisy, messy, partial	Test with real input and let the model repair it
Speed	Unnoticed	Dead air on a call	Measure, preload, fill
Confidence	Always answers	Confidently wrong	Thresholds and refusals
Actions	Books the meeting	Books the wrong one	Confirmation in code
Failures	Never happen	Happen on Tuesday	Fallbacks
After launch	Done	Just starting	Read logs and iterate

None of these fixes are glamorous, and none of them make a demo look better. All of them make the difference between a system a business tries once and one it relies on every day.

Have an AI demo that needs to survive real users? That's exactly what I do.