Über das Projekt:
ZeroFit is a fitness app for time-crunched professionals. It asks two questions: How do you feel? How long do you have? and delivers safe personalized workouts across three modes: main sessions (5-60 min), Bedside (lying down), and Microworkouts (5 min, targeted to body area and mood).
Das Team
Samana Upadhyaya (MSc Translational Neuroscience, research assistant at the University of Heidelberg.) brings domain insight, product vision, and direct access to target users through her work. Deep Mehta (Master's Mechatronics, founder of Toothlens) brings engineering rigor and startup growth experience. From Toothlens, Deep learned what to look for in a market, what to build first, and how to scale without burning out the team or users. Both based in Heilbronn.
Die Idee
ZeroFit user answers two questions: How do you feel? How long do you have? Because these are the main to constraints determining if and how a person will workout on a particular day. Based on this input, the app delivers personalized workouts across three modes. Main workouts run 5 to 60 minutes. Bedside mode offers 61 exercises doable lying down, designed for recovery and wind-down while on bed in a scrollable format. Microworkouts take 5 minutes: you say what body area needs relief and how you feel, the app picks moves, and tracks mood before and after. 994 exercises cuurentlyin teh databank across all the three modes. All are curated and reviewed by a certified trainer. The core algorithm is deterministic: every exercise is mapped to goal, energy, injury exclusions, equipment, and fitness level. Same inputs always return a safe result. The app removes guilt triggers: no streaks, no locked badges, no progress bars. Live on web with 100 beta testers. Plans to implement AI based behavioural porfilinmg of teh users to give better recommendations, motivation strategies etc.
Customers
Primary: time-crunched professionals in corporate and research settings. Samana identified this segment working as a research assistant at Heidelberg University, observing researchers and lab staff working 6+ hour sessions in one chair, unable to take breaks. They want to move but don't know how, don't know if it's safe, and don't have time for gyms. Secondary: corporate wellness programs. Corporate wellness budgets are increasing year-over-year as employers recognize burnout prevention as a retention lever.
Market Chances
Seventy-nine percent of beginner fitness app users hit a blocker within 90 days: which workout to pick, whether it's safe, or starting too hard and being unable to move by day four. This segment is motivated but stuck. Five minutes of guided, personalized exercise beats doing something blindly and can prevent long-term injury. The corporate wellness segment offers a direct B2B path: labs, research institutes, and tech companies already budget for employee health programs and are actively seeking solutions for desk-bound workers.
Financing Plan and Profitability
Currently bootstrapped. We've invested €422 to date and are self-funding through the beta phase. Revenue model: freemium with two subscription tiers (Lite €3.99/mo, Plus €9.99/mo, full exercise library in both). Licensing costs for MuscleWiki are fixed per exercise; we scale revenue by user acquisition. Unit economics: token-based cashback system rewards consistent users, reducing churn. We plan to raise pre-seed funding by end of 2026 to accelerate user acquisition and expand the corporate wellness channel. Target break-even at 5,000 active users (currently building toward 1,000 in the next 12 months).
Sustainability
The core mechanic is mood-aware, position-aware fitness. This solves a real problem: beginners don't know how to start safely, and guilt-driven apps drive them away. We offer the ability to move safely, whenever, wherever. The algorithm prioritizes safety first, learns preferences second. The business model aligns with user retention: the more a user moves and tracks mood, the more they see their own progress and stay. No artificial streaks or FOMO mechanics.
Customers
Primary: time-crunched professionals in corporate and research settings. Samana identified this segment working as a research assistant at Heidelberg University, observing researchers and lab staff working 6+ hour sessions in one chair, unable to take breaks. They want to move but don't know how, don't know if it's safe, and don't have time for gyms. Secondary: corporate wellness programs. Corporate wellness budgets are increasing year-over-year as employers recognize burnout prevention as a retention lever.
Market Chances
Seventy-nine percent of beginner fitness app users hit a blocker within 90 days: which workout to pick, whether it's safe, or starting too hard and being unable to move by day four. This segment is motivated but stuck. Five minutes of guided, personalized exercise beats doing something blindly and can prevent long-term injury. The corporate wellness segment offers a direct B2B path: labs, research institutes, and tech companies already budget for employee health programs and are actively seeking solutions for desk-bound workers.
Financing Plan and Profitability
Currently bootstrapped. We've invested €422 to date and are self-funding through the beta phase. Revenue model: freemium with two subscription tiers (Lite €3.99/mo, Plus €9.99/mo, full exercise library in both). Licensing costs for MuscleWiki are fixed per exercise; we scale revenue by user acquisition. Unit economics: token-based cashback system rewards consistent users, reducing churn. We plan to raise pre-seed funding by end of 2026 to accelerate user acquisition and expand the corporate wellness channel. Target break-even at 5,000 active users (currently building toward 1,000 in the next 12 months).
Sustainability
The core mechanic is mood-aware, position-aware fitness. This solves a real problem: beginners don't know how to start safely, and guilt-driven apps drive them away. We offer the ability to move safely, whenever, wherever. The algorithm prioritizes safety first, learns preferences second. The business model aligns with user retention: the more a user moves and tracks mood, the more they see their own progress and stay. No artificial streaks or FOMO mechanics.