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Rails & React.js Development for Curated Shopping Recommendation Engine

Services Covered:

Custom Software Development • AI and Machine Learning • Cloud Consulting and Services • Website Development
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Industry
Industry

E-commerce & Retail

Duration
Durationinfo-circle

21 months

Budget
Budget

200K - 999K

Start Date
Start Date

1 May 2016

Team Size
Team Size

6-10

Engagement Model
Engagement Model

Nearshore

Project Capability Score (PCS)

gauge meter
73/100
Good

Project Capability Score (PCS) estimates how capable Monterail 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 United Kingdom

Project Summary

Monterail developed both the frontend and backend for a curated shopping recommendation platform using React.js and Ruby on Rails, hosted on AWS with Elasticsearch. The solution includes responsive UI, tailored questionnaires, custom assistant components and a machine learning component to pair users with relevant products. The platform achieves high engagement, with around 80% of users completing the questionnaire, and Monterail’s team adapted to the client's processes and tooling.

Key Challenges

  • Scale up the existing codebase
  • Speed up development and increase engineering capacity

Project Deliverables

  • React.js frontend
  • Ruby on Rails backend
  • Responsive web application
  • Machine learning recommendation component
  • Elasticsearch-backed search/indexing
  • Questionnaires and custom assistant components
  • Integration with publishers and retailer pricing comparisons

Project Solution

Monterail developed a complex React.js frontend and an intricate Ruby on Rails backend for a web-based product recommendation platform. They implemented a responsive design, deployed and scaled services using Amazon CloudFront and EC2, and hosted search/indexing with Elasticsearch. The team built tailored questionnaires and custom assistant components per product category and integrated a machine learning component to match individual users with relevant content, while collecting semantic and behavioral data to monitor and refine recommendations.

Project Outcome

  • 80% of users interacting with the questionnaire complete it, yielding a very high engagement rate
  • Positive conversion and engagement metrics across the platform
  • Efficient, reliable vendor team that adapts to the client's processes and tooling (Trello, Slack) and maintains communication

Platforms

  • WebWeb
  • CloudCloud

Tech Stack

  • React.jsReact.js
  • Ruby on RailsRuby on Rails
  • Amazon EC2Amazon EC2
  • Amazon Web Services (AWS)Amazon Web Services (AWS)
  • ElasticsearchElasticsearch

Client Endorsement

Overall Review Rating

4.88star

Timeliness

Cost Rating

Willing to Refer

Quality of Deliverables

“The platform performs with an impressive 80% user engagement rate. Monterail’s personable team has a broad skillset and adjusts to preferred work methods. Unlike other vendors, they care about project success over a paycheck and offer flexible costs to account for exchange rates.”

Anonymous

CTO

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

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