Selected problem - Andespace

At Universidad de los Andes, many students have long gaps between classes, but it’s hard to consistently find actually available and suitable spaces (free classrooms, quiet study areas, or comfortable places to rest). During peak hours, the best spots fill up quickly and there is no reliable, centralized way to confirm real-time availability. As a result, students waste time walking around, experience stress, end up in overcrowded areas, and lose productivity or rest time.

Proposed solution

We will build a mobile application that reduces uncertainty about campus spaces and helps students discover, verify, and reach locations that fit their needs (study, group work, tutoring, or rest) during their free time. The solution integrates three core capabilities and a clear data pipeline:

Core data pipeline (University API → availability engine)

• The app will consume an official university API that provides course offerings and their assigned classrooms (i.e., which subjects are taught and in which rooms, by time slot).
• The system will analyze the schedule data by hour/time block, identify the rooms that are occupied, and discard them.
• The remaining rooms become the app’s candidate set of “free rooms” for that specific time window.
• This availability engine continuously updates as schedules change, ensuring the user sees results that reflect institutional data.

Key features

1. Real-time availability + verification

   • The baseline availability comes from the University API schedule (occupied rooms removed, remaining rooms shown as available for that time window).
   • To handle real-world mismatches (e.g., a room appears free in the schedule but is actually occupied), the app can optionally incorporate live signals such as user check-ins or quick occupancy reports (with timestamps) to show statuses like “likely available / possibly occupied.”

2. Route optimization (Point A → Point B)

   • Once the student selects a room/space, the app provides the fastest/most efficient route to get there.
   • This is designed for short decision windows between buildings, floors, and tight class transitions.

3. Personalized content and space recommendations

   • Using stored app data such as previous reservations/searches, frequently visited locations, and the student’s uploaded schedule, the app recommends rooms/spaces that match preferences and context.
   • Recommendations consider time available and proximity to the next class (e.g., “This room is near your next lecture and matches your usual preferences.”)

Value proposition

• For students: “Find a space that is truly available and fits your purpose—fast—and get there efficiently.”
  This saves time, reduces stress, improves productivity/rest, and lowers frustration caused by unreliable availability information.
• For the campus: better distribution of student flow across spaces and more efficient usage of existing infrastructure through data-driven insights.

Revenue model

The business model monetizes the app through two main streams:

1. In-app advertising (primary stream)

   • Ads shown inside the app (e.g., banners or native placements in discovery screens).
   • Likely advertisers: campus-relevant businesses (cafés, food, tutoring, stationery) and university-adjacent services.
   • Scales with daily usage during academic weeks.

2. B2B analytics / insights from aggregated app data (secondary stream)

   • The app generates aggregated, anonymized insights (search patterns, demand peaks, popular areas, route flow trends).
   • These insights can be sold as monthly subscriptions or reports to approved stakeholders (e.g., campus operations, student services), supporting decisions on scheduling, signage, and space planning—while protecting individual privacy through aggregation.

Summary

This solution addresses a clear student pain point—wasting time and experiencing uncertainty when trying to find available campus rooms—by using the University course-offering API to compute availability (occupied rooms removed, free rooms recommended), and by adding route optimization plus personalized recommendations to make the experience fast, reliable, and student-centered.

Uniandes-based revenue estimate (short vs. long term)

Campus size used (addressable market): ~18,300 students (Over 14,500 undergraduate + More than 3,800 graduate).
Monetization: in-app ads + B2B aggregated analytics subscriptions.

Horizon	Adoption (MAU as % of ~18,300)	MAU (users)	Ads revenue (USD/month)	Analytics revenue (USD/month)	Total revenue (USD/month)	Total revenue (USD/year)
Short term (≈ 6 months)	10% – 25%	1,830 – 4,575	200 – 2,800	0 – 3,000	200 – 5,800	2,400 – 69,600
Long term (≈ 3 years)	40% – 70%	7,320 – 12,810	1,800 – 9,900	3,000 – 20,000	4,800 – 29,900	57,600 – 358,800

What these ranges assume (typical for a campus app):

• Ads scale with MAU + daily sessions (e.g., ~4–10 ad impressions/user/day, ~18–22 active days/month, eCPM roughly in the low single digits).
• Analytics grows from ~0–2 early clients to ~3–6+ clients later, paying monthly for aggregated utilization/demand insights.