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Podknife Podcast Platform — Full-Stack Development, UI/UX & AI/ML

Services Covered:

Custom Software Development • AI and Machine Learning • Website Development • Software Maintenance and Support
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Industry
Industry

Media

Duration
Duration

73 months

Budget
Budget

50K - 199K

Client Size
Start Date

1 February 2017

Team Size
Team Size

1-5 Employees

Engagement Model
Engagement Model

Nearshore

Project Capability Score (PCS)

gauge meter
79/100
Good

Project Capability Score (PCS) estimates how capable Ekohe 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

Podknife

mapBoston, United States of America

Project Summary

Ekohe built Podknife, a web-based podcast discovery platform, providing UI/UX design, business consulting, full-stack development, data science, and AI/ML features. The vendor implemented a React front end and Ruby on Rails back end hosted on AWS, used NLP for personalized recommendations, and provided ongoing maintenance. The work led to improved engagement, expanded active users, increased content and creator collaborations, and growing advertiser interest.

Key Challenges

  • Taking the site from wireframes to launch
  • Considering how advances in technology could enhance the project
  • Making continuous improvements and performing maintenance over time

Project Deliverables

  • Business consulting (agile product roadmap)
  • UX/UI designs and prototypes
  • Full-stack web application (React front end, Ruby on Rails back end) hosted on AWS
  • NLP-based search and personalized content curation (AI/ML)
  • Data science insights and analytics
  • Ongoing maintenance and platform support

Project Solution

Ekohe provided business consulting and executed an agile product roadmap, designed intuitive UX/UI prototypes, and implemented a full-stack web solution using React JS for the front end and Ruby on Rails for the back end hosted on AWS. They implemented NLP algorithms for dynamic search and personalized content curation, performed data-science analysis on podcast data, and delivered ongoing maintenance and performance optimizations.

Project Outcome

  • Improved user engagement metrics, including longer sessions, increased search activity, and higher content consumption rates
  • Expansion of the active user base and increased frequency of return visits
  • Positive qualitative user feedback on the intuitive interface and personalized recommendations
  • Growth in the number of podcasts and increased collaborations with content creators
  • Growing interest from advertisers and sponsors, indicating potential revenue opportunities

Platforms

  • WebWeb

Tech Stack

  • React.jsReact.js
  • Ruby on RailsRuby on Rails
  • Amazon Web Services (AWS)Amazon Web Services (AWS)

Client Endorsement

Overall Review Rating

5star

Timeliness

Cost Rating

Willing to Refer

Quality of Deliverables

“Thanks to Ekohe, the client increased user engagement metrics, platform sessions, and content consumption rates. They also received positive user feedback, expanded their user base, and boosted advertiser and sponsor interest. Ekohe's dedication and expertise in data science were phenomenal.”

Matthew Barnard

Founder & CEO

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

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