Optimized Academic Planning

Automates end to end process with AI, using resources efficiently (classrooms, teachers, etc.) to build quality collaborative schedules.


Optimization is also a part of the experience

Considers university’s community interests to build an academic planning that efficiently resolves the multiple needs and expectations, with process efficiency and effective results.

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Student demand forecast

Generates accurate course demand projections, helping on budget control.

Institutional regulations

Compliance with business rules, considering schedule quality criterias for professors and students.

Centralized collaboration

Enables for collaborative work among areas, keeping control and centralization of information.

Efficient simulation

Permite simulaciones para generar el mínimo de grupos, optimizando la planta física y la disponibilidad docente.

Decision making

Includes AI engines for supporting manual user decisions based on roles and permissions.

Dynamic workflows

Establishes workflows for requesting and assigning physical facilities, extending them to the university community.

Student enrollment forecasting

Simulates students' curriculum progress, generating robust course demand estimates.

  • Simulation of students’ progress patterns under different scenarios.
  • Modeling of specific curriculum progress rules as per study plan.
  • Estimates the closure of on-going academic periods based on historical patterns.

Demand collaborative edition

It collaboratively communicates, validates and adjusts course demand estimates.

  • Intuitive navigation through study plans to confirm or adjust demand estimates.
  • It configures demand grouping between equivalent courses to generate savings at group generation. 
  • Considers cross-cutting demands and elective subjects, for demand distribution in accordance with teaching types and modes

Groups and schedule simulation

It automates the generation of schedules using algorithms to minimize the opening of groups, improving the quality of schedules.

  • It plans based on student demand forecasts. 
  • It recognizes the relevant populations (new admissions, graduation candidates, deserters, etc.). 
  • Takes into account the profile, availability, and contracts of faculties.
  • It considers the capacity of physical facilities

Collaborative Schedule Editing

It allows manual adjustment of schedules in a collaborative manner, with support of intelligent recommendation engines.

  • It navigates through the different programming dimensions: students, groups, rooms and professors. 
  • It validates rules automatically, controlling schedule conflicts, faculty availability, and facilities. 
  • It suggests changes which are feasible based on available faculty/room options through the recommendation engine. 
  • It allows versioning for simulation scenarios, for planning based on multiple assumptions.

Group management

Streamlines and automates the management of quotas available for students in the enrollment process.

  • Enables for collaborative work across diverse  roles involved in the process.
  • Manages group quotas, making them visible for enrollment. 
  • Incorporates validations to ensure changes aligned to policies when offering quotas for students. 
  • All changes made by users into the groups get audited and logged.

Resource management and reservation

It extends to the university community the possibility of booking physical facilities for specific activities, providing

  • Definition of requester and administrator users (no user limits). 
  • Identification of activity typologies to be managed. 
  • Sending automatic emails to notify requesters their booking status. 
  • Providing intelligent recommendations about facilities, considering the existing bookings in the academic programming. 
  • Mobile-first design.

Data Management


Students Management

Student forecast

Demand Editor

Schedule Management


Schedule Editor





AI engines connected to user interface modules, integrated to the digital ecosystem.

100% cloud native

Infrastructure designed to fully exploit the capabilities of cloud computing.

SaaS managed by Foris

High availability enrollment service, managed and supported by Foris.

AWS best practices

AWS Cloud Architects; partnership since 2017.


Any questions?

Please contact our team, to learn more about our solutions.