Decision quality, not response quality
Most AI answers “What should I do?” in a single pass. Nearbeat is built around a fundamentally different question.
“What happens if I choose A instead of B — this week, this year, and five years from now?”
That requires modeling consequences and trade-offs explicitly, rather than generating plausible-sounding prose. Every recommendation shows which dimensions moved, by how much, and why.
The personalization moat
The product launches broad and shallow — any decision type, all five dimensions, lightweight scoring — and lets your own history determine where it invests in depth. Two things make this possible that a stateless chatbot session doesn’t have: a rolling, per-dimension confidence score for you specifically, and a record of what you decided and how it actually turned out.
The outcome feedback loop
Decision made → outcome check-in (scheduled) → was the recommendation right? → dimension weights, agent confidence, and decision-type depth all update for next time.