Why We Use 5 AI Models Instead of One

When we started building Bounce Watch, we did what everyone does: picked one AI model and built everything around it. It worked. Until it didn't. Some tasks needed nuance. Others needed raw speed. Some required real-time web access. Others needed structured pattern detection. No single model excelled at all of these. So we started orchestrating. The problem with single-model architecture If you're building a B2B product that uses AI, you've probably experienced this: your model is great at generating text but terrible at structured extraction. Or it's fast but shallow. Or it's thorough but too expensive to run on every request. The instinct is to upgrade to the latest model and hope it covers everything. It won't. What multi-model looks like in practice Here's how we think about it. Each task in our pipeline has different requirements: Nuanced analysis — When we generate company insights, we need a model that understands context, can make connections, and writes like a human analyst. S