Web Development
LAB325 is a full-service digital products engineering firm. We're a technological partner for startups and big brands in developing and achieving their additional profit with the help of technological solutions. Our core focus includes: Mobile App development from scratch (iOS, Android); Web development (React.js, Next.js, TypeScript); Web3/Blockchain development; Delivering AI/ML solutions; (Bot & AI development) Internet of Things (IoT) solutions; UI/UX app & web design; Prototyping & concept development; Product & software design; Why LAB325? Most of our contracts are long-term engagements: 2+ years; We have been building successful products for 7 years; Our clients’ MVPs get their 5M+ investments; We need 45 days to develop a project from the idea to MVP; We have built a 30k+ tech community; Products built by us are used by over 3M users worldwide and have won several awards;
Industry: Appliances, Electrical, and Electronics Manufacturing About the project NFT Screensaver is an innovative application that gives a user the possibility to put the most popular NFTs on the market as a screensaver on Android TV. Solution LAB325 started with defining the process and revisiting all the problems to build the most smooth and user-friendly way of delivery of NFTs directly to customers' TVs. We did research on top players in the NFT market, and the possibility of cooperation with the most known NFT marketplaces. We created an AI-based product that allows us to choose and download the most popular NFTs and put them as a screensaver. The app targets the main pain points they have: - Find the most popular NFTs on the market; - Download NFTs; - Put NFT as a screensaver; - Pick your favorite NFTs from a thousand of the most popular on the market. Gazer NFT Screensaver helps NFT lovers care for their users in 3 steps: - AI-based NFT service; - Built-in database of NFT collections; - Possibility to download NFTs and put them as a screensaver. Technologies Swift Kotlin for Android; Back-end: node.js; API: GraphQL; In-app search: Elasticsearch.
About the project IzziFit is a fitness app that incorporates gamification for iOS and Android. Users can gamify their workouts to reach personal wellness goals faster & easier. Solution We created an app that is great for those who hesitate between playing a game or doing fitness. It’s a personal fitness assistant with a rich collection of market-standard features combined with a slots game – an all-time favorite. Technologies Swift for iOS, Kotlin for Android; Back-end: node.js; Database: MongoDB, PostgreSQL, RabbitMQ; API: GraphQL, Rest API; Infrastructure: AWS; Main modules Built-in game interface implemented without using specialized game engines; Wellness program recommendations based on user goals and parameters; High-quality in-app content: training videos and articles; Activity & emotional tracking and personal statistics; Pace calculator in iOS version; Activity reminders and push notifications for drinking water and sleeping well; Multilanguage user interface and data; Integration with SDKs: Facebook, Firebase, Appsflyer, Google Ads, AdMob; In-app purchases and subscription payments on App Store and Google Play Market; Social login;
About the project The client was looking to create a mobile app that allows real-time climate control of a vehicle while using the hardware provided by the client. Solution LAB325 made research of top applications on the market that perform the same function. The team created a clickable prototype to ensure the client’s business requirements are addressed. Server architecture was developed to meet the high-load requirements. Further, the application was built based on the above. LAB325 developed a mobile app that allows a user to: ensure a comfortable temperature in the user’s vehicle; Manage climate control remotely & on schedule. Technologies Swift for iOS Back-end: node.js, Database: MongoDB, PostgreSQL, RabbitMQ, ClickHouse API: GraphQL, Rest API, Protocol Buffers (Google Protobuf) In-app search: Elasticsearch Infrastructure: AWS, Kubernetes, Deep Learning AMI
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