case study

Tanoto

Client

CodeLink

industry

HRTech

services

AI ML

Custom Software Development

Overview

Tanoto is an app designed to help users practice interviews and receive feedback on their answers' content, pace, eye-tracking, and facial emotions.

Team Model

  • Front-end Developer
  • Machine Learning Developer
  • Back-end Developer
  • Technical Lead
  • Quality Assurance Engineer
  • Product Designer
  • Product Owner
  • Technology

    NextJS

    NestJS

    ReactJS

    Python

    Whisper

    Face Landmark Detection

    MediaPipe Face Detection

    Emotion Classification

    Text to Speech

    Platform

  • Web
  • Challenge

    How can we assist job applicants in improving their interview and presentation skills?

    Request

    CodeLink had developed their own propriety text-to-speech and speech-to-text AI model. They wanted to run a design sprint to experiment with how they could turn the model into a commercial product. Stakeholders tasked CodeLink's internal teams with running a design sprint and proposing a final tested prototype. The prototype would then be built into an MVP product release.

    Engagement Model

    The CodeLink internal team worked as a fully autonomous team to facilitate and run the design sprint, test the prototype, and build out the MVP followed by a fully functional V1 release of the product.

    Engagement Length and Scale

    The MVP phase took 6 weeks to complete and release for beta testing. We then run another 6-week phase to implement basic authentication and profile management for the V1 release of the product.

    Project Outcome

    To start the project, we conducted a 1-week virtual Design Sprint workshop. Our goal was to establish the product's goals, vision, and value proposition. We created a low-fi prototype and held in-person user testing to gain insights into user needs and how to meet them. Our tests validated the product concept, and we began development. To develop the app, the team used a home-grown Text-to-Speech solution and Whisper for Speech-to-Text. We researched and applied real-time face landmark tracking models, such as MediaPipe, and then researched and applied real-time emotion classification models. The final product was fully developed and released to the market.

    Tags

    Design Sprint

    Autonomous Team

    Artificial Intelligence

    Machine Learning

    Product Design

    Product Development

    Prototype Testing

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