Millions of personal injury cases are settled in the U.S. every year, as few go to trial — but the vast majority are kept under wraps. This leaves lawyers guessing what they should propose as a settlement price, oftentimes resulting in victims being undercompensated.

It’s what led Rami Karabibar to launch EvenUp, a startup that taps AI to generate legal documents to assess injury cases. The platform, aimed at customers in the legal field, attempts to use raw case files, including medical records, police reports and bills, to create letters arguing for proposed compensation.

“We’re on a mission to level the playing field in personal injury cases,” Karabibar, who previously worked across private equity, venture capital and venture-backed startups, said.

Karabibar co-founded EvenUp with Ray Mieszaniec, a two-time entrepreneur, whose father was permanently disabled after being hit by a car involved in a police chase. Mieszaniec’s family got just 10% of the average payout for that type of accident — partly because their lawyer didn’t know what the appropriate compensation should be.

EvenUp aims to tackle all categories of personal injury cases, including motor vehicle accidents, police brutality, child abuse and even natural disasters. To do this, Karabibar, Mieszaniec and EvenUp’s third co-founder, Saam Mashhad (a former litigator), built a database of private settlements — including hundreds of thousands of medical records — and trained an AI to estimate fair compensation based on the details of each case.

EvenUp’s platform extracts the relevant info from documents and organizes them into templated “demand packages,” which state the legal and factual basis for a personal injury claim and include a demand for compensation. Designed to be a self-service solution for lawyers, paralegal staff and law firms, EvenUp summarizes notes and copies of raw records into medical digests “optimized for