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EndorsedendorsedOngoing

App & Software Development for Machine Learning Startup

Services Covered:

Custom Software Development
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Industry
Industry

Software Development

Duration
Durationinfo-circle

40 months

Budget
Budget

200K - 999K

Start Date
Start Date

1 January 2015

Team Size
Team Size

11-15

Engagement Model
Engagement Model

Nearshore

Project Capability Score (PCS)

gauge meter
77/100
Good

Project Capability Score (PCS) estimates how capable inoxoft 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

Confidential

map Israel

Project Summary

INOXOFT developed an Azure-based cloud platform (ASP.NET backend, AngularJS frontend) and Android/iOS applications for an Israeli machine-learning startup focused on emotion and motion recognition. The vendor delivered internal tools for voice-recording upload and emotion tracking and external mobile apps used for events and hospital workflows. The engagement improved the machine learning engine's quality, UX, and internal productivity.

Key Challenges

  • Needed to develop internal tools and external solutions to support machine- and deep-learning technology
  • Required a tool to feed different types of data to the machine learning engine to make it more accurate and faster

Project Deliverables

  • Azure-based cloud solution and backend
  • Internal emotion-tracking software for uploading and analyzing voice recordings
  • Android and iOS mobile applications (consumer/event and hospital-facing apps)

Project Solution

INOXOFT built a cloud solution using Microsoft Azure with an ASP.NET backend and an AngularJS frontend, and developed multiple Android and iOS applications. They delivered an internal tool to upload and analyze voice recordings to improve the machine-learning engine and created mobile apps for analytics/tracking (including a hospital-facing app that collects text and voice inputs for doctor predictions). The vendor supplied a multi-role team (developers, QA, managers) and provided ongoing product and delivery support.

Project Outcome

  • Significantly improved the quality and efficiency of the machine learning engine
  • Delivered stable internal tools that improved employee tracking and performance
  • Produced smooth, well-received mobile UX and reliable applications used by staff and external users

Platforms

  • MobileMobile

Tech Stack

  • AzureAzure
  • ASP.NETASP.NET
  • AngularJSAngularJS
  • AndroidAndroid
  • iOSiOS

Client Endorsement

Overall Review Rating

5star

Timeliness

Cost Rating

Willing to Refer

Quality of Deliverables

“The tools perform smoothly, improving the engine's quality and boosting internal productivity. INOXOFT communicates clearly about deliveries, delivers within requested timelines, and responds promptly to inquiries. Their development team is always offering helpful suggestions.”

Serhiy Andreev

Senior Manager

Note: This endorsement is based on publicly available client feedback from external review sources.

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