The future cannot be known. Only estimated.

WHAT WE ARE BUILDING

Most consequential decisions rest on a guess about the future. A statistical model could turn that guess into a number with a range around it, but building one takes expert time that few decisions can justify. So most of them are still made on instinct.

Praevidere builds tools that help people reason about the future. They take the form of AI agents that do the modeling themselves: they gather the evidence, map what drives the outcome, and return the answer as a distribution with a source behind every number.

Nightingale is the first of these agents, and it works on insurance underwriting. We started there because insurers already pay for estimates of future loss, and because the claims on written policies show, over time, whether those estimates were right.

WHY NOT GENERIC AI

A general-purpose assistant can read the same evidence and write a convincing answer, sometimes with a number that looks like a probability. Nothing connects that number to the evidence, though, and there is no way to see how it was produced.

Questions about the future are hard in a specific way: the evidence is messy, and each event happens once, so you cannot rerun the year to check. That is why our method is Bayesian. The agent states its assumptions up front, weighs the evidence against them, and shows what the answer depends on.

The agent also builds a model, which is more than a prediction. Ptolemy predicted the planets well, but only Newton’s model could say what would happen if Mars were heavier. A decision is that kind of question, since the whole point of deciding is to change something.

Everything the agent does stays open. It writes down its plan, the evidence log, the graph, and the samples as it goes, so you can check any step before relying on the answer. When the evidence cannot support a number, it gives none and tells you what is missing.

The evidence can be public, your own, or both. You choose the model provider, and the whole workload can run on your infrastructure.

WHO WE ARE

We are three founders: an applied scientist, a quantitative analyst, and a jurist. Between us we have built and stress-tested frontier AI systems, priced uncertainty in asset management, and worked inside regulated institutions. All three fields expect numbers to survive an audit, and we hold our agents to that standard.