Overview
Fuzu, a leading East African career platform, partnered with Banao Technologies to enhance their user experience through a powerful AI-driven recommendation engine. Our goal was to simplify user onboarding, utilize uploaded resumes for intelligent data extraction, and deliver accurate, personalized job recommendations to millions of job seekers across Kenya, Uganda, and Nigeria.
Industry
Recruitment & Career PlatformBusiness type
Online Job Platform
Services
Machine Learning & AI Development | Natural Language Processing | Custom Software Engineering
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Impact After Launch
After integrating the AI recommendation engine, Fuzu achieved a seamless onboarding flow, smarter candidate-job matching, and measurable engagement growth. The new system analyzes text data from resumes and cover letters to classify skills, education, and industry—enabling precise, scalable job recommendations that significantly improved platform performance.
Key outcomes after deployment
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AI-powered job recommendations delivered to users,
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automated document classifications processed, and
0%
increase in job application click-through rate observed.
Challenge
Fuzu’s onboarding required users to fill lengthy forms to generate quality recommendations, which led to drop-offs and incomplete registrations. The platform also needed to leverage the large volume of user-uploaded resumes and cover letters more effectively to improve recommendation accuracy.
Our Solution
Banao implemented a scalable recommendation engine powered by Natural Language Processing (NLP) and Machine Learning. The system automatically extracts and classifies data from user-uploaded text documents—identifying education level, skills, and experience—to instantly generate accurate job recommendations. This reduced friction during onboarding while improving recommendation precision.
Features Implemented:
- Automated data extraction from resumes and cover letters
- Education and skills classification using NLP
- Personalized job recommendations using behavioral data
- Collaborative filtering and hybrid neural network models
- Scalable ML infrastructure for continuous model improvement
Key Features Implemented
An intelligent, self-improving recommendation system delivering personalized job matches and insights.
AI-Powered Resume Parsing
Automatically analyzes user-uploaded resumes and cover letters to extract structured data such as skills, education, and experience.
Smart Job Recommendations
Delivers real-time, personalized job suggestions using a hybrid model that combines collaborative filtering and neural networks.
Frictionless Onboarding
Reduces the need for lengthy user questionnaires by using document-based data extraction to auto-fill profiles.
Scalable ML Architecture
Supports continuous data-driven improvements and the addition of new features such as language modeling and user behavior prediction.
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Frequently Asked Questions
How did the AI improve Fuzu’s onboarding?
The onboarding process became faster and easier as users could simply upload their resumes. The system extracted data automatically to complete profiles and generate recommendations.
What accuracy did the education classifier achieve?
The education level classification model achieved an impressive 90% accuracy rate.
How much did user engagement improve?
The click-through rate for job applications increased by 30% after deploying the new recommendation engine.
Is the platform scalable for future ML use cases?
Yes, the ML framework was designed to expand, supporting future NLP use cases like job application assistance and resume quality feedback.