BeEpic is an early-stage startup founded by two co-founders. The company aims to make digital gratitude tipping and appreciation simple, accessible, and relevant for modern service interactions.
The product focuses on situations where people want to thank someone for a service, even if there was no direct interaction. This can include delivery drivers, housekeeping teams, hospitality staff, and other service workers whose contribution is often visible only after the service is completed.
BeEpic’s growth model combines two directions. On the receiver side, the platform is designed for enterprise-driven expansion through integrations with hotels, logistics companies, and delivery systems. On the giver side, BeEpic focuses on city-led growth in tipping-friendly locations such as hospitality venues, hotels, and everyday service hotspots.
Business context
BeEpic approached Geniusee at the bootstrapped MVP stage. Their team needed a full-cycle engineering partner capable of taking the product from design through architecture, mobile development, and cloud infrastructure.
The product required more than a standard tipping flow. BeEpic needed to connect a giver and a receiver asynchronously, using location and timing as the foundation for matching. For example, a person could leave gratitude after receiving a service, while the platform would help identify the right service provider based on geolocation and related context.
To make this possible, the MVP needed a reliable mobile experience, accurate location tracking, structured user flows, and backend logic capable of processing spatial data with high precision.
Validate a new digital gratitude model
Enable asynchronous giver-receiver matching
Build accurate geolocation filtering
Work around device and third-party limitations
Solutions we implemented
MVP architecture for geolocation-based gratitude
Geniusee designed and developed the technical foundation for a mobile MVP centered on asynchronous, location-based gratitude. The architecture supported core user actions, data processing, and future scalability for integrations with hospitality, delivery, and logistics systems.
Custom geolocation tracking logic
To address device and third-party limitations, our engineers implemented a custom geolocation-tracking solution. This helped the app capture and process location data more reliably across mobile environments.
Spatial filtering for accurate location processing
We developed filtering logic to determine and process user locations with higher precision. This functionality became the core layer for matching gratitude with the right context, supporting BeEpic’s model, in which the giver and receiver may not interact directly.
Backend logic for asynchronous data handling
The backend was designed to store, manage, and process geolocation-related data in a structured way. This allowed the system to support asynchronous gratitude flows and maintain the data foundation needed for future product iterations.
Mobile-first UX/UI design
The design team shaped a clear mobile experience for onboarding, authorization, profile management, gratitude flows, and communication. The interface was created to make a new behavior feel natural for users from the first interaction.
Delivery governance and scope control
Geniusee used a structured Scrum process with two-week sprints, regular planning, refinement, daily stand-ups, sprint reviews, and retrospectives. To prevent scope creep, the team also applied formal change control, assessing each scope adjustment against the timeline and budget impact before implementation.
Registration and authorization
The app includes user registration and secure authorization flows powered by Auth0. This gives users a simple way to access the product while supporting a reliable identity layer for future scaling.
Profile management
Users can create and manage personal profiles inside the app. This feature supports the platform’s user structure and prepares the product for more advanced giver and receiver experiences.
Real-time geolocation
The app captures user location data to support gratitude flows connected to specific places and service moments. This functionality is central to BeEpic’s product logic.
Geolocation filtering
The system applies spatial filtering to process geographic coordinates and improve matching accuracy. This helps the platform connect gratitude with the right location context.
Messaging
The MVP includes messaging functionality to support communication between users inside the platform.
Push notifications
Push notifications help keep users informed about important updates and interactions, supporting engagement across the mobile experience.
✅ MVP prepared for investor validation
Geniusee delivered an MVP that allowed BeEpic to continue investor conversations with a working product foundation rather than only a concept.
✅ Core product logic implemented
The team developed the key technical layer behind BeEpic’s idea: asynchronous, geolocation-based gratitude supported by custom location tracking and spatial filtering.
✅ Mobile experience ready for early users
The MVP includes the essential user-facing flows needed to test the product with the target audience, including registration, profiles, geolocation, messaging, and notifications.
✅ Scalable foundation for future integrations
The architecture was built with future enterprise-driven growth in mind, including potential integrations with hotel, delivery, and logistics systems.
✅ Delivery completed within the expected scope
The project followed a controlled Scrum process with disciplined scope management, helping the client stay aligned with MVP priorities, timing, and budget expectations.
Challenge: Validate a new digital gratitude model
BeEpic’s core functionality depends on accurate geolocation tracking. During development, the team faced limitations with mobile devices and third-party tools, which affected how consistently the app could capture and process location data in real-world use cases.
Solution: Enable asynchronous giver-receiver matching
Geniusee implemented a custom geolocation tracking solution to support the app’s key gratitude flow. This allowed the product to process location data more reliably and build the foundation for asynchronous matching between givers and receivers.
