endorsedCompleted

AI-Enhanced Mobile App for Facial Landmark Recognition

Services Covered:

Mobile Application Development

Industry

Software Development

Duration

8 months

Budget

Confidential

Start Date

1 March 2020

Team Size

1-5 Employees

Engagement Model

Nearshore

Project Capability Score (PCS)

79/100

Good

Project Capability Score (PCS) estimates how capable Light IT Global is of successfully delivering a project like this, based on its past experience, track record, and ability to handle similar work. Learn more about PCS

Client

TestFort

Ukraine

Project Summary

Light IT developed an AI-enhanced mobile application for facial landmark recognition and analysis for a testing-services client. The vendor delivered the backend, mobile app, and ML components using Python and Flutter, followed Scrum with bi-weekly sprints, and completed the project within the agreed timeline and budget; the app reduced routine work for end-users.

Key Challenges

Create an AI-enhanced mobile app for facial landmark recognition, analysis, and advanced reporting
Require strong AI expertise to implement complex functionality

Project Deliverables

Backend development
Mobile application (Flutter)
AI engine / machine learning component
App documentation and prototype

Project Solution

Light IT provided planning, requirements gathering, documentation, prototyping, and full-stack development. The team built the backend and machine-learning components in Python and developed the mobile app in Flutter, coordinating via Scrum with bi-weekly sprints and using tools such as Jira, Confluence, Slack, Zoom, and GitHub.

Project Outcome

App delivered according to specifications
Delivered within estimated time and budget
App reduced routine work for end-users

Platforms

Mobile
Web

Tech Stack

Python
Flutter

Client Endorsement

Overall Review Rating

Timeliness

Cost Rating

Willing to Refer

Quality of Deliverables

“Light IT was able to perform well during the collaboration and the app was delivered according to the client's specifications and within the agreed timeline and budget. The output helped in reducing the routine work for the end-users. They utilized Scrum methodology with bi-weekly Sprints.”
Evgeniya Rubezhanskaya
Process Quality Manager
Date: 03/11/2021