As a player in the AI industry, we are firm believers in the value it can deliver — when integrated correctly into analytics pipelines at insurance firms.
You can't trust chatbots to generate reliable predictions. But you can leverage generative AI to make large, complex, and intricate legal documents accessible to purpose-built predictive algorithms. That's exactly what we do.
And the value goes beyond a rough estimate of possible damages. We've built mathematical modeling into the entire dispute-prediction pipeline, so every prediction ships with a calibrated error measurement — not just a single number, but a set of analytics you can reason with.
This makes Canotera a building block in the decision chain at every stage: when taking on a case, throughout its evolving life cycle, and all the way to closure.
Blog
Related articles.
Can You Trust LLMs to Predict the Outcome of an Insurance Claim?
Language models generate verbal predictions that sound sensible — but they were trained to sound logical, not to be mathematically sound.
Neural-Symbolic Models for Legal Outcome Prediction
Generative AI is a text engine, not a crystal ball. To forecast litigation outcomes accurately, you must fundamentally separate the act of reading a claim file from the mathematics of predicting its cost.
Generation Is Not Prediction
Large language models are built to produce plausible text, not accurate forecasts. Confusing a statistical parrot for a mathematical pricing engine is a fast way to misprice your entire claims portfolio.
Want to talk to an executive?
Press, partners, investors, candidates — the inbox is monitored. Tell us who you are and we'll route it to the right person within two business days.