Training and validating data models requires considerable human input to generate accurate and effective outputs.
Therefore, Defined.ai challenged us to help their engineering and design teams build a crowd-sourcing mobile app where users can complete daily tasks that train AI models.
The first step in training, testing, and validating AI involves feeding data into a computable algorithm to create predictive models and evaluate their accuracy. With that in mind, we helped Defined.ai build a mobile application to get the necessary human input to make this possible on a broader scale.
The app could match users with specific mobile-friendly tasks based on their demographic, language, and regional preferences. Crowd users' tasks included audio and image recognition, text-to-speech analysis, and sentiment evaluation.
Engaging via our staff-augmentation model, our team worked closely with Defined.ai's design and engineering departments to develop a mobile React Native application for users worldwide on both Android and iOS devices.
While Defined.ai focused on scaling and growth through various internal projects, our team was able to jump in as an engineering extension of their tech team. We delivered a mobile app that enabled the company's pool of crowd members to contribute data that aided the smooth running of AI models.
The DefinedCrowd offered fast and accurate training data to their clients, including companies such as BMW, Mastercard, Accenture, Jibo, Nuance, and Voicebox ― among other Fortune 500 companies.
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