Automated clustering replaced manual training data creation for faster setup
We couldn’t afford AI trainers manually creating training data, so we built a clustering pipeline that analyzed historical customer conversations already sitting in CRMs like Salesforce. This let us automatically find the 100 or 200 most common questions and show customers all 30,000 actual ways users asked questions like where’s my order. Competitors were using FAQ pages and having people manually write 50 to 100 variations, which didn’t reflect real customer language. Our approach gave faster time to value because we used actual conversation data instead of imagined variations, and customers could see their real top questions before even talking to us.
— How Ultimate evolved from hundreds of supervised models to UltimateGPT · The Edge