A prediction engine for legal AI. It forecasts where the next rulings land and what controls firms will be forced to adopt, weighted by who actually sets the rules, and it grades every call against what really happened.
The overview: legal AI reaches a firm through four layers, run through a three-part engine, out to two clear calls per issue — stand up the control, and put AI on the work.
See the overview → Go deeperThe mechanics: the two curves, the three orders of prediction, authority-over-volume scoring, and how the engine grades itself against its own record.
Read the method → See itThe actual product inside Legal AI OS: the build-now radar map, the force-ranked queue, and the calibration record on screen.
Take the tour → See the dataWhere the signal comes from: four kinds of information — rules, capability, market, vendors — flowing into five meters, and where they overlap.
Map the streams → Trust itEvery document the radar weighs, all 25, oldest to newest, each with what it is and why it earns a place in the knowledge base.
Check the sources → How I built itOne real problem taken apart start to finish: discover, design, build, evaluate, repair, track. The process, shown instead of described.
See how it was built → One scrollThe whole picture in one page: the three-part stack, the eleven fault lines, the scoring tiers, and how evidence earns its way in.
Read it end to end →