Everyone’s racing to fine-tune LLMs. But here’s a thought: what if the real breakthrough isn’t just in model weights, but in the way we shape behavioral context and output formatting in real-time?
That’s what SpatzAI does.
Every time a team member issues a Caution, Objection, or Stop through the Spatz Chat app, they’re not just resolving a micro-conflict; they’re helping the system learn how to object to objectionable behavior, fairly.
That’s real-world, supervised fine-tuning.
Traditional fine-tuning is like training a chef in a kitchen once.
SpatzAI is like that chef learning every night from the team’s feedback, and adjusting their plating, tone, and timing on the fly.
Spatz trains the LLM with structured data: what behavior occurred, how objection was raised, what accountability was required (Acknowledgement? Apology?), how long it took to resolve, and how the other party responded. This gives the model something few systems have: real-time, objective human behavioral data, tied to escalating levels of objection.
And what’s more, it teaches the AI how to format its output properly.
Because tone matters. Phrasing matters. And in workplace conflict, the way we say something often carries more weight than what we say.
SpatzAI doesn’t just feed the model new data; it teaches it how to verbalize a caution versus a formal objection. It trains models to respond fairly, not just compute fluently.
In short, SpatzAI is formatting not just the conversation, but the future of AI-powered teamwork.
We’re not just moderating behavior.
We’re curating fairness, one resolved spat at a time.
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