Why Most AI Startups Ship Broken Products (And How to Avoid It)

We've worked with dozens of AI startups, and we see the same mistake repeatedly: founders mistake a working demo for a shippable product.

The demo works in controlled conditions. It impresses investors. It gets the funding. Then it hits production — and falls apart.

The Demo-Production Gap

AI products face challenges that traditional software doesn't. Latency spikes. Context window limits. Hallucinations under edge cases. Prompt injection vulnerabilities. Cost at scale.

None of these show up in a demo. All of them show up in production.

What We Do Differently

At Tinlance, we build for production from day one. That means thinking about failure modes before we write the first line of inference code. It means building evals. It means designing your system to degrade gracefully when the model surprises you.

The result is products that actually ship — and stay shipped.