enosys.ai, Inc.

AI infrastructure for regulated domains

We build platforms that combine formal reasoning with AI for compliance and planning problems where every answer has to be correct.

12
institution catalogs encoded
1,900+
degree programs encoded
36,000+
courses in catalog
< 24 hours
to encode any college, on request
Our Approach
Formal reasoning meets AI

Language models handle natural language well. They are unreliable when an answer depends on checking hundreds of rules at once, so we keep that part of the work out of the model.

⟨⟩

Requirements as code

We turn degree rules, regulations and policies into machine-readable specifications and validate them. A requirement the engine cannot check is rejected when it loads.

⊞

One engine decides

A single component evaluates the rules and decides whether a requirement is met. The degree audit and the planner both call it, so they always agree.

◎

AI as interface

Students ask questions in plain language. The model turns them into operations on the student's plan and explains what changed.

All three are running in production today.

In production

My Academic Planner

Turns a university catalog into degree requirements the engine can check, then builds graduation plans that respect prerequisites. Students work with it through an AI advisor.

Request a demo Explore the platform
Onboarding
Any college, on request, in under 24 hours

Requirements, general education, credit policy, calendar and branding are all loaded from data, so nothing is coded per school.

Everything we encode comes from the published catalog, so a school can be added on request without an integration project. There is nothing to connect or provision.

Who it's for
Institutional users

Students use the planner day to day. The staff who depend on it being correct work in the registrar's office and in advising and academic policy.

Registrars

See what a catalog or policy change breaks before it is published.

Advising offices

Routine plan checks are automated, which frees advising hours for students who need a conversation.

Academic policy

Exceptions and waivers are modelled directly, and the result before the exception stays visible.

Institutional research

Every plan records how each requirement was evaluated, so any result can be inspected afterwards.

Provosts and deans

Estimates of time to degree and course demand come from the same model that validates plans.

IT and integration

We read the published catalog, so nothing needs to be connected to the student information system.

Student collaborations
Built with student engineers

Student engineering teams at two universities are building on the platform.

App Dev Club

University of Maryland

Three student engineering teams from the club are building new planner features for the UMD catalog.

AI/ML Club

San José State University

Four student teams are running applied ML projects on the planner, from matching interests to courses to evaluating the AI advisor.

Nothing is wired into a student information system, so a student team can build on the platform without access to university systems.