Scaling South Africa’s Youth Employment Campaign with AI: A 1.2M Applicant Success Story

Result

Our AI-powered system matched 1.2 million applicants with 600,000 internships, eliminating the need for 100+ employees to process data manually. This streamlined the program, saved costs, and delivered Gauteng’s largest youth employment initiative on time.

Challenge

Picture this: The government of Gauteng, South Africa’s largest province, launches the Nasi iSpaan Campaign to combat youth unemployment. Initially, the program aimed to place 8,000 interns, but when 1.2 million applications poured in, it became clear the demand far exceeded expectations.

In response, the government ambitiously expanded the program to 600,000 internship placements—but the sheer scale overwhelmed the system.

Manual processing by over 100 employees wasn’t just inefficient—it was impossible. With deadlines looming and data quality issues piling up, the program was at risk of collapsing entirely.

This wasn’t just about logistics. It was about the futures of hundreds of thousands of young South Africans. Failure wasn’t an option.

That’s when they called us.

Our Approach

Turning Overload Into Opportunity

We started by taming the beast: 1.2 million applications riddled with missing and inconsistent data. Using advanced data cleaning algorithms, we built a foundation of trust in the data, correcting errors and filling gaps at scale.

Clustering With Purpose

Next, we designed a skills-based clustering system that grouped applicants by their abilities and career goals. This wasn’t just matching numbers to names—it was creating meaningful connections between candidates and opportunities using Machine Learning Algorithms.

An Engine Built to Scale

With Python, TensorFlow, and cloud infrastructure, we created an automated matching system that processed applications at lightning speed. Scalable, precise, and error-proof, it replaced manual processing entirely, handling millions of data points with ease.

Solution

Data Cleaning

• Automatically detected and corrected errors in massive datasets.

• Resolved missing or incorrectly captured information at scale.

Skills-Based Clustering

• Developed machine learning models to group applicants by skills and career aspirations.

Automated Matching Engine

• Designed an end-to-end system capable of pairing 1.2 million applicants with 600,000 positions in record time.

• Integrated validation checks to ensure high-quality matches, reducing employer complaints.

Impact

Timely Delivery: Enabled South Africa’s Department of Labour to complete the Nasi iSpaan campaign on schedule, avoiding overtime and costly delays.

Massive Cost Savings: Eliminated the need for 100+ employees to process data manually.

Better Matches: Improved matching accuracy by algorithmically aligning skills with career paths, setting participants up for success.

Why We’re Different

This wasn’t just about building a system—it was about building the right system.

Where others saw a mountain of problems, we saw an opportunity to deliver a solution that was smarter, faster, and more cost-effective.

We don’t just write code. We solve big, messy problems with clarity and precision. Whether it’s matching millions of people to the right opportunities or scaling a system to handle exponential growth, we get results where it matters most.

Your challenge is our expertise. Let’s make it happen.