Infrastructure · Reliability
Making bulk AI generation reliable under load
An asynchronous processing pipeline using Redis and BullMQ for high-volume content-generation workloads.
Project details, identifiers, visuals, and metrics have been generalized to respect confidentiality.
The challenge
Turning complexity into a product people can trust.
Bulk generation created long-running requests, provider limits, and failure scenarios that a synchronous request-response flow could not handle safely.
My approach
Designed from the system out.
Moved generation into queued background jobs with controlled worker concurrency.
Designed progress states, retries, and failure handling so users could track work without blocking the application.
Separated web traffic from compute-heavy generation to improve resilience and operational control.
System view
[Add or refine architecture diagram]
Impact
What changed.
[Add peak job volume or throughput]
[Add failure-rate improvement]
[Add user waiting-time improvement]