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.
Geometric Machine Learning on Resolved Cases
Large language models are word guessers, not calculators. To predict the financial outcome of a lawsuit, you must separate the extraction of text from the mathematics of risk.
Negotiating Settlements From Data, Not Gut
Plaintiff attorneys walk into mediation armed with structured verdicts and funding algorithms. Defense teams walk in with a spreadsheet and a gut feeling. It is time to eliminate this asymmetry at the negotiation table.
A Forecast for Every Open Claim: The API
A forecast isn't useful if it lives in a silo. We built the Canotera API to inject calibrated settlement ranges and reserve deltas directly into the systems where adjusters already work.
Measuring Calibration: Why We Publish Error Rates
A claim prediction is useless if you do not know how often the model is wrong. Publishing error rates forces a transition from guessing to actual risk management.
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