We need to talk about open source prediction modelling.
If you're slightly technical this is for you. https://github.com/666ghj/MiroFish Before trying to sell something to real people, they are using AI to practice the sales pitch first. Imagine you made a new toy and wanted to sell it. Instead of going straight to shop owners and hoping they like it, you first build fake practice shop owners based on real information. These practice buyers are made from things like: - what real companies say - what problems they have - what their bosses care about - what they might spend money on - what objections they might have Then the AI runs a pretend sales conversation. For example: “What happens if we tell this buyer that Sales Machine helps with CRM automation?” The AI then says things like: “This buyer would probably care about saving time.” “This buyer might worry about cost.” “This buyer would need proof that it actually works.” “This buyer would respond better if you talk about revenue, not automation.” Then all the results get saved in Airtable, so the team can compare different sales messages. Instead of guessing what customers want to hear, they test different pitches against AI versions of real buyers first. The goal is to find out: - which message works best - which problems buyers care about - what objections will come up - what proof points make people trust the offer So when the real salesperson finally speaks to a real buyer, they are much better prepared. If you want to know more, comment below.