The original title is about predicting when clients will pay invoices using AI. Let me rewrite this for a mobile feed.

Short summary

Build per-client payment timing models using quantile distributions and Bayesian priors instead of blanket reminders; send nudges only when they historically drive earlier payment. Calculate reminder lift to suppress unnecessary follow-ups for reliable-but-slow clients. Use an LLM to generate escalated reminder tone calibrated to each client's history, creating a self-improving feedback loop.

• •Per-client distribution modeling beats blanket reminders—track quantiles and reminder lift to optimize when and whether to nudge
• •Suppressing unnecessary reminders is as valuable as sending well-timed ones; every needless nudge trains clients to ignore you
• •LLM-generated tone should escalate based on client history and relationship, not a global template

Generated with AI, which can make mistakes.