Human intelligence.

Machines that miss the point

Today's agents can pass exams and still lose a customer in three messages. They answer the words and miss the intent. They push for a close when the person needs reassurance. They reply in formal English to someone who wrote in slang. Raw intelligence is no longer the gap. Reading people is.

Intent, tone, and context are data

A message carries more than its text: who is asking, what they actually want, how they feel about it, what was said before, and what silence means. Humans process all of that without noticing. We treat it as a first-class engineering problem: signals to be detected, represented, and acted on, with the same rigor models apply to syntax.

Speak like the person, not at them

People trust what sounds like them. That means mirroring language, register, and pace: replying to slang in slang, to formality with formality, to a hesitant buyer with patience instead of pressure. An agent that gets this right doesn't feel like software. It feels like a colleague who listens.

The difference between a tool and a teammate

Companies don't run on data alone. They run on conversations: selling, supporting, negotiating, apologizing, persuading. Agents that can't hold those conversations stay tools, forever supervised. Agents that can, become teammates people actually hand work to. That is the layer we mean by human intelligence, and it's what makes the rest of the stack usable by real people.