endorsedCompleted

Product Recommendation Engine & Behavioral Analytics for Kandy

Services Covered:

Custom Software Development • AI and Machine Learning • API Development

Industry

E-commerce & Retail

Duration

9 months

Budget

10K - 49K

Start Date

1 February 2025

Team Size

6-10 Employees

Engagement Model

Offshore

Project Capability Score (PCS)

87/100

Strong

Project Capability Score (PCS) estimates how capable Acquaint Softtech Private Limited 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

Kandy

Lithuania

Project Summary

Acquaint Softtech built a Python-based product recommendation engine and behavioral analytics platform for an e-commerce growth firm (Kandy). They designed data pipelines, ML models, APIs for real-time recommendations, and monitoring dashboards, which led to improved customer engagement, higher average order value, and increased repeat purchases.

Key Challenges

Develop a smart product recommendation system that personalizes suggestions based on user behavior
Create a scalable backend platform for behavioral analytics across partner brand stores
Improve the customer shopping journey through personalization at key moments in browsing and checkout

Project Deliverables

Python-based recommendation engine for personalized product suggestions
Behavioral data analysis pipelines processing user interaction events
Product similarity scoring models
APIs enabling real-time recommendations within the storefront
Monitoring dashboards to evaluate recommendation performance

Project Solution

Acquaint Softtech designed and implemented a scalable backend in Python to process behavioral e-commerce data, built machine learning models for product similarity and recommendation, and exposed dynamic recommendation APIs for storefront integration. They also created monitoring dashboards to evaluate performance and rolled out the system in phases so partner stores could test and adopt recommendations incrementally.

Project Outcome

Increased customer engagement with suggested products after deployment
Improved average order value through relevant complementary product recommendations
Higher repeat purchase rates due to personalized suggestions aligned with past behavior

Platforms

Web
API/Integration Platform

Tech Stack

Python
REST API

Client Endorsement

Overall Review Rating

Timeliness

Cost Rating

Willing to Refer

Quality of Deliverables

“The system helped the client see clear improvements in customer engagement, average order value, and repeat purchases. Acquaint Softtech Private Limited managed the project in clear phases and maintained regular communication with the client. The team was also highly responsive and adaptable.”
Noah Putna
Co-Founder
Date: 03/16/2026