Project

Project Overview

We worked with a global research initiative sponsored by Google and Amazon, collaborating with top-tier universities around the world. Their field researchers often worked in remote locations, sometimes completely offline, to collect data and observations relevant to their studies.

The Challenge

• Offline Data Collection: Researchers needed robust tools to gather structured and unstructured information (notes, images, videos, sensor data, location tracking) without relying on an internet connection.
• Inefficient Spreadsheets: Previously, data was recorded in Excel sheets, making it cumbersome to maintain, manage, and analyze.
• Limited Off-the-Shelf Solutions: Attempts to adopt a generic system proved costly and inflexible. Custom feature requests were slow, expensive, and required significant compromise on their unique requirements.
• Scalability Concerns: They needed a system that could be easily deployed across different platforms (mobile and web) without incurring high development costs.

Our Approach

• Discovery & Requirements Gathering: We worked closely with the end-client to identify their exact data collection needs and the field conditions under which researchers would operate.
• Custom Architecture: Recognizing the need for offline functionality and flexible data structures, we planned a solution that combined a mobile app for front-end data collection with a scalable API and backend.
• Iterative Development: By adopting an agile approach, we could deliver functional prototypes to the end-client, gather feedback, and refine the system throughout the project lifecycle.
• Cross-platform Development: Be cost-effective and scalable across multiple platforms (iOS, Android, Web).

The Solution

• Tailor-Made Application & API: Built to accommodate a variety of data types (text, multimedia, sensor data) and to function reliably offline, syncing seamlessly once internet access was restored.
• Offline Capability & Data Syncing: Ensured researchers could gather high-quality data in remote environments without connectivity.
• User-Centric Design: Streamlined interfaces and workflows allowed non-technical researchers to capture information quickly and accurately.
• Seamless Integration: Prepared collected data for ingestion into machine learning models, speeding up the research and training process.

The Results

• On-Time, On-Budget Delivery: The project was completed according to schedule and cost expectations.
• High Satisfaction: The custom solution addressed all the end-client’s needs more effectively than previous spreadsheet or off-the-shelf solutions.
• Increased Research Efficiency: Researchers could now collect, manage, and analyze data more effectively, ultimately enhancing the quality and speed of their studies.

Technologies & Tools Used

• Flutter for the cross-platform mobile application
• C# .NET for the backend services and API
• AWS for cloud infrastructure and deployment