Posts on what we actually think.
Essays on conformal prediction, social inflation, and where generative AI helps and where it doesn't. Written by the team.
Conformal Prediction for Claims: Ranges, Not Point Guesses
A machine learning model that predicts a precise settlement dollar amount for a casualty claim is lying to you. Litigation is probabilistic, and your forecasting models must mathematically respect that reality.
Why Day-One Reserves Are Systematically Wrong
Setting an initial reserve is an exercise in institutional guesswork. Adjusters are forced to pick a number before the facts materialize, creating a compounding cycle of misallocated defense spend and missed negotiation leverage.
Inside the Canotera Pipeline: From Case File to Forecast
Processing multi-gigabyte case files requires strict architectural boundaries. We separate the language models that read medicals from the mathematical models that calculate settlement ranges.
Why LLMs Can't Predict Legal Outcomes
A language model generates text that looks like an answer. It does not calculate probabilities based on historical claim geometries. Confusing the two is a fast way to misprice your reserves.
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