Legal AI platforms: validate the core before scaling delivery

Legal document review is a high-cost workflow where AI can shorten cycles—but only if accuracy and verification are designed in from the start. This interview describes building a “brain in the jar”: an early AI core developed before adding enterprise layers, so teams can experiment, measure accuracy, and learn what the model can and cannot do. It highlights the practical loop between developers and legal partners, including prompt engineering and systematic validation of AI outputs, with experimentation often done in Python. In one case, the approach reportedly reduced review time by up to 75%. Tech leaders can use this to de-risk AI programs by separating capability proof from later scale, governance, and collaboration concerns.