There's a massive gap between a working AI demo and a production AI system. We've crossed that gap over 20 times, and the lessons are remarkably consistent.
First: monitoring is not optional. Every AI system drifts. Models that performed at 98% accuracy in testing will degrade as real-world data shifts. You need automated evaluation pipelines that continuously test your model against fresh data and alert you when performance drops. We've caught issues within hours that would have taken weeks to notice without monitoring.
Second: build for failure. AI systems will produce wrong outputs. The question is how your system handles it. Every pipeline needs graceful fallbacks — confidence thresholds that trigger human review, retry logic for transient errors, and circuit breakers that prevent cascading failures.
Third: latency matters more than you think. A model that takes 5 seconds to respond might be fine in testing but unusable in production. Optimize early. Techniques like model distillation, quantization, and caching can often reduce latency by 10x without meaningful accuracy loss.
Fourth: data quality is everything. The most sophisticated model architecture can't compensate for noisy, biased, or incomplete training data. Invest in data quality before investing in model complexity.
Fifth: start simple. The best production AI systems we've built started with the simplest approach that could work. Complex architectures are harder to debug, harder to maintain, and often don't outperform simpler alternatives. Add complexity only when you have evidence it's needed.
These principles aren't glamorous, but they're what separate AI systems that run businesses from AI systems that run demos.
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