How Breezy learns

When you bring on a new employee, you don’t just throw them into the deep end. You give them examples, show them how things work, correct them when they make mistakes, and they get better over time. That’s pretty much how AI learns too.

Breezy doesn’t come with built-in knowledge, it needs data to learn. Data is like the training material you’d give a new staff member. The more high-quality examples you give it, the better it gets.

 If you want it to suggest bookings, you show it booking history and patterns

If you want an AI to recognise customer complaints, you give it lots of examples of past complaints

 If you want it to suggest bookings, you show it booking history and patterns..

Patterns, not rules

Unlike traditional software that follows fixed rules (like a calculator), AI finds patterns. Let’s say you have 1,000 examples of customer messages and which ones led to cancellations. Breezy AI will:

Read through all of them

Start to notice that certain words or timings (like “frustrated” or messages sent after long delays) often come before cancellations

Learn to predict cancellations in future based on similar messages

It’s not memorising, it’s spotting patterns.

Practice makes perfect

AI gets trained through trial and error:

Learn to predict cancellations in future based on similar messages

It gets told whether those predictions were right or wrong

It adjusts itself to improve next time

This loop happens millions of times during training.

Different types of learning

There are a few ways AI can learn:

Supervised learning: You give it the data and the correct answers, ( “This message = cancellation”).

Unsupervised learning: You give it data without labels, and it finds its own structure, (“Group similar customer profiles together”)

Reinforcement learning: It learns from trying things and getting feedback. Think of it like training a dog, “Good job!” or “Try again”.

If you’re using tools like Breezy, this is happening behind the scenes. It learns from the types of questions your customers ask, how you respond, and what leads to successful bookings, all so it can be more helpful over time.