AI-Powered Personalized Recommendation Engine

Smart course and job matching for learners and professionals

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About

Industry

EdTech, Career Development

Application Type

AI-powered Web Platform

Core Functionality

Personalized recommendations, user profiling, skill mapping, market-driven insights

The client operates within the EdTech and Career Development ecosystem, serving students, job seekers, and working professionals by offering digital learning and career guidance. Their earlier recommendation system relied on static logic and generic suggestions that rarely matched user goals or abilities.

They needed a smarter engine that understood individual skill levels, learning behavior, career intent, and market demand. The system had to map users to relevant courses, certifications, and job roles while adapting to real-time feedback.

Technically, the platform required ML-driven recommendation models, behavioral profiling, skill mapping algorithms, integrations with course catalogs and job boards, along with a scalable architecture capable of serving millions of suggestions seamlessly.

Results

The recommendation engine significantly improved learning outcomes and job readiness:

Accuracy

4 times improvement over rule-based recommendations

Completion

60% increase in course completion rates

Placement

35% faster job placement cycles

Engagement

80% rise in user interaction

Their team understood our needs, solved complex technical challenges and delivered with clarity and speed.

Contact Our Team

Challenges

Why it Mattered?

Learners and jobseekers depend on guidance tied to real skills and real opportunities. Personalized recommendations reduce trial and error and help users pursue learning paths that directly support employability.

Our Approach

We built a multi-model recommendation engine powered by skill data, behavior insights, and real-time market signals.

Combined collaborative filtering, content similarity and behavioral analysis

User profiles updated continuously based on activity, assessments and achievements

LMS, job portals and course provider feeds merged into a unified pipeline

Mapped user abilities against in-demand competencies

Tools for counselors and learning teams to review and fine-tune recommendations

Validation layers to remove bias and keep suggestions balanced

Our Tools:

ML Models

Collaborative filtering

Content-based filtering

Neural recommendation networks

NLP

BERT

Sentence Transformers

Backend

Python

FastAPI

Node.js

Frontend

React

Next.js

Databases

PostgreSQL

ElasticSearch

Redis

DevOps

Docker

AWS Lambda

ECS

CloudWatch

Integrations

LinkedIn Jobs API

LMS APIs

Course provider APIs

Before & After

The utilization of the AI-powered job recommendation system result in higher user engagement, accuracy, and completion rate.

Feature/Metric	Before – Rule-Based System	After – AI-Powered Recommendations
Recommendation Accuracy	~25–30%	85%+ personalized accuracy
Job Placement Speed	Slow and manual	35% faster placements
Course Completion Rate	Low due to poor matching	60% increase
User Engagement	Generic suggestions	80% increase
Counselling Effort	High manual workload	50% reduction

Testimonial

A Team That Turned Our Vision into a Real, Intelligent Product

The AI recommendation engine they built has transformed how we guide learners and job seekers. What impressed us most was how quickly the system began delivering accurate, personalized matches at scale. Their team understood our needs, solved complex technical challenges and delivered with clarity and speed. It’s been a game changer for our users and our internal teams alike.

Chief Product Officer