Case Study: Bugcrowd unlocks AI workflows with Temporal

Bugcrowd Unlocks AI Workflows with Temporal

Bugcrowd, a crowdsourced cybersecurity platform, needed to replace a manual hacker-matching process and move beyond the limits of its Ruby monolith. The team wanted to use machine learning to improve engagement selection, while also increasing development speed and reducing the operational burden of services, queues, databases, and cron jobs. To do this, Bugcrowd turned to Temporal Technologies and Temporal Cloud.

With Temporal Technologies, Bugcrowd built three coordinated workflows to orchestrate database lookup, ML-based hacker matching, and invitation handling through child workflows, signals, and timers. This modular approach eliminated much of the manual work, making new hacker engagements and crowds 2x faster to launch, increasing capacity by 400%, saving about 15 engineering hours per week, and cutting overall downtime by 50%.

Bugcrowd