What challenges did Apna face before AI implementation?

Match millions of blue-collar and professional workers with relevant jobs and upskilling opportunities at scale. Continuously improve matching models as employer and candidate needs change. Detect and remove abusive or fraudulent content on a large community platform.

Source

GCPScaled productionEvidence: Medium65/100

Apna: Optimizing upskilling and job opportunities for millions across India with Vertex AI

Use case typeAI platformUpdated Jun 13, 2026

Since launching in 2019, Apna has grown into India's largest jobs and professional networking platform by deploying an AI algorithm to connect millions of workers to relevant job opportunities. Apna built a cloud-native marketplace and job-matching platform on Google Cloud, using Vertex AI, Google Kubernetes Engine, and BigQuery to support continual model improvement, large-scale analytics, and platform safety.

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Organization: Apna
Industry: Education
Location: India
Published: January 2024

Planned next steps

The source says the organization aims to achieve Time: −20%.
The source says the organization aims to achieve Time: Up to 40% lower.

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Time: 20% decrease

Google Cloud Customer StoriesJan 1, 2024Customer storyInferred claimMedium evidence strength

Estimated 20% reduction in time to create AI models.

Normalized claim

Time: 40% decrease

Google Cloud Customer StoriesJan 1, 2024Customer storyInferred claimMedium evidence strength

Estimated up to 40% savings in DevOps time.

Normalized claim

Quantified impact: 60%

Google Cloud Customer StoriesJan 1, 2024Customer storyInferred claimMedium evidence strength

Up to 60% of inappropriate content removed daily.

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Apna
Provider: GCP
Maturity: Scaled Production
Linked source: Google Cloud Customer Stories

Match millions of blue-collar and professional workers with relevant jobs and upskilling opportunities at scale

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 4

1AI matchmaking
2Content moderation
3MLOps

Built a proprietary AI job-matching algorithm on Vertex AI.
Used BigQuery pipelines to process up to 500 million user interactions per day for analytics and modeling.
Used GKE to run cloud-native microservices and deploy features quickly.
Used Vertex AI-driven ML models to detect abusive or fraudulent behavior through keyword detection and improve platform safety.
Ran multiple AI experiments per day to fine-tune matching performance.

Technologies

Vertex AI Google Kubernetes Engine BigQuery Cloud Monitoring

Up to seven AI experiments per day enabled.

Architecture

Apna's platform uses Google Kubernetes Engine for cloud-native microservices, BigQuery for high-scale data pipelines and analytics, and Vertex AI for model development, deployment, and daily experimentation. Vertex AI also supports ML models used to detect abusive or fraudulent content.

Sources & evidence1

Evidence: Medium65/100Evidence strength