Project AI - Scalable Gardening Mobile App
Client
AI (A Hybrid Gardening App)Category
AI Development, Mobile App Development, Backend DevelopmentDate
2024Challenge
The client aimed to create a robust and scalable mobile application that could assist users with plant care and provide predictive billing capabilities for sellers. They required a platform that seamlessly integrated Generative AI Large Language Models (LLMs) to enhance the user experience and offer advanced features.
Process
Our process included prioritising ideas for an MVP, low and high-fidelity prototype creation, conducting technical feasibility assessments, and then commencing development. Our team utilised a technology stack that included Flutter/Dart for the mobile app, TypeScript for the web components, AWS and Heroku for cloud infrastructure, Firebase for authentication and real-time database, Node.js and Python for backend microservices, and OpenAI for integrating LLMs.
We assembled and managed a talented team of developers, fostering a collaborative and productive work environment. We followed Agile methodologies, conducting sprint ceremonies and iteratively improving the platform based on user feedback. Security measures and scalability strategies were implemented to ensure data protection and the ability to handle increasing user loads.
Solution
We utilised various technologies including AWS, Node.js, and Python to build a functional proof of concept now being tested for performance, user experience, and product/market fit. The AI platform was successfully developed, featuring a robust mobile app that integrated LLMs to provide Plant AI assistance and predictive billing capabilities for sellers.
The platform's microservices architecture, built with Node.js and PostgreSQL, ensured high performance and reliability. User feedback was incorporated to enhance the user experience, and performance optimisations were implemented to reduce latency. Cross-functional collaboration with design, marketing, and sales teams ensured cohesive product development and go-to-market strategies which are now being actioned.
Results
The next steps include feedback iterations, identifying product/market fit, and further technical integrations including Stripe, OpenAI, and Royal Mail as we progress on the project lifecycle.