Buzzhire’s platform lets companies in the hospitality and delivery industries search for temporary staff who are immediately available. An extensive database of workers matches businesses with workers based on location, availability, and experience. Workers are notified of the vacancy and can register their interest in the position.
The app guides workers through the whole process of the shift. They are advised on the time they should leave their home, how to reach the job location, how to perform the shift, and when to log in and out. At the end of each shift, workers can rate the employer, and the employer can rate the worker. This rating system helps users make informed decisions in the future.
The mobile app is key to the success of Buzzhire’s operation. Workers are notified of available shifts through the app with no need to see the webpage. Buzzhire wanted to raise the overall standard of their mobile app and add new functionalities to keep them ahead of the market.
At the beginning of the partnership, it was clear that changes would need to be made to the operational system to keep up with ongoing regulations. Technical debt had to be reduced and maintenance was essential to continue providing a well-performing application. One of the main goals in the roadmap was to expand a locally operating system to the whole UK.
A priority throughout this project was to reduce technological debt, replace the old mobile app, and introduce national coverage for the system. In order to achieve this, we updated the versions of Python, React, Node, and RDS PostgreSQL DB that were being used. To ensure high-quality code for the future, we introduced monitoring for which we used the applications Prometheus and Grafana.
Frontend improvements were made using TypeScript, ESlint, Prettier, Enzyme, and JEST. On the backend, by changing the existing uWSGi server to Gunicorn, we stopped memory leaking and the CPU utilization from being overwhelmed. Fabric3 mechanisms were also added to automate deployment on the development, staging, production environments.
As the collaboration continues, we’re starting to produce new features for commands.
Kyriba
A feature based on OpenAI GPT model that allows users to explain any Python snippet directly within the app. Any portion of code can be interpreted without the need to leave the tool. The solution also supports code generation from a supplied natural language prompt.
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meet.mba
A solution integrated with generative AI providers providing users with an AI assistant through a designated smart-chat. Users can enjoy all the generative AI features while maintaining the privacy and security of their data.
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Podimo
An application for audio lovers and audio content creators allowing listening and uploading podcasts and audiobooks implemented with the Learning to Rank Machine Learning model for repositioning search results and Semantic Search capabilities
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