Today was all about building and training the LLM behind Socle Intelligence, probably one of the most ambitious parts of Socle so far.

The goal isn’t to make another AI chatbot that spits out generic advice. I’m building a system that can actually understand what’s happening inside a business by connecting customer experiences, employee performance, feedback patterns, and historical data.

For example, if a customer says their food took forever but their server was amazing, the model needs to recognize two completely different signals: a potential operational issue and an employee who deserves recognition.I’ve been working on the model’s reasoning, training it to interpret feedback in context, experimenting with different parameters, and building the infrastructure that will eventually let business owners ask questions about their own data.

The hardest part is getting the AI to distinguish between an isolated complaint and an actual emerging pattern, without making conclusions that the evidence doesn’t support.

I’m also exploring local model deployment rather than relying entirely on external AI APIs, which adds another layer of complexity.

The end goal is simple: turn thousands of individual interactions into intelligence that businesses can actually act on.Still plenty of work left, but this is where Socle starts moving beyond collecting data and toward understanding it.