We build platforms that combine formal reasoning with AI to solve complex compliance and planning problems where accuracy isn't optional.
Language models are good at language. They are unreliable when an answer depends on checking hundreds of discrete rules at once, so that part of the work does not run through a model.
Degree rules, regulations and policies become validated, machine-readable specifications. If the engine cannot check a requirement, it does not load.
One component decides whether a requirement is met, by evaluating the rules rather than estimating. The audit and the planner both call it, so a student never gets two answers.
People just ask. The model turns plain questions into real operations on the student's own plan, and explains what changed.
All three are running in production today.
Turns a university catalog into machine-checkable degree requirements, then builds prerequisite-aware graduation plans, with an AI advisor as the interface.
Requirements, general education, credit policy, calendar and branding are all resolved from data at run time.
Everything we encode comes from the published catalog, so a school can be added on request instead of after an integration project. There is nothing to connect and nothing to provision first.
Students use the planner day to day. The people who need it to be correct sit in the registrar's office, in advising, and in academic policy.
See what a catalog or policy change breaks before it is published.
Routine plan checking stops eating the hours that belong to students who need judgement.
Exceptions and waivers are modelled directly, with the original verdict still visible underneath.
Every plan carries the derivation behind it, so any result can be inspected afterwards.
Time-to-degree and course demand, estimated from the same model that validates plans.
Nothing has to be wired into the student information system. We read your published catalog.