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AI/ML2023

SmartMatch AI Engine

A comprehensive case study on how we transformed this project and delivered measurable business results.

Duration

7 months

Budget

$320K

Team Size

8 people

Status

Completed

SmartMatch AI Engine

Featured Image

The Challenge

Our client faced significant challenges that were impacting their business growth and user experience. We needed to develop a comprehensive solution that addressed multiple pain points simultaneously.

1

Building ML model from scratch with limited historical data

2

Achieving personalization at scale

3

Balancing accuracy with diversity in recommendations

4

Cold-start problem for new users

Our Solution

We developed an integrated strategy combining design excellence, modern technology, and strategic thinking to deliver a comprehensive solution.

Trained deep learning model on 50M+ user interactions

Implemented collaborative filtering with content-based hybrid approach

A/B tested 50+ variations to optimize diversity-accuracy tradeoff

Bootstrapped new users with demographic and behavior clustering

The Results

35%

increase in user engagement

94%

ML model accuracy on holdout test set

2.5x

revenue uplift from improved recommendations

Processing

10M+ recommendations daily

Services

DevelopmentAI/MLData ScienceAnalytics

Technology Stack

Frontend

  • React
  • Next.js
  • Tailwind CSS

Backend

  • Node.js
  • PostgreSQL
  • GraphQL

DevOps

  • AWS
  • Docker
  • GitHub Actions

Tools

  • Figma
  • Jira
  • Sentry

Key Learnings

User-Centric Design

Deep user research led to insights that shaped every design decision.

Scalable Architecture

Building with scalability in mind from the start enabled exponential growth.

Cross-Functional Collaboration

Close partnership between design, development, and product teams was crucial.

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