Overdrive
A queue-based AI analytics backend processing 10,000+ influencer profiles for a Chicago-based marketing agency.
- Industry
- Marketing / influencer analytics
- Region
- United States (Chicago)
- Stage
- Delivered client product
- What we built
- Queue-based AI analytics backend
Figures reflect designed capacity from the build; live production metrics are verified per engagement.
The problem
Influencer campaigns at scale require collecting and analyzing data on thousands of profiles, then coordinating teams around that intelligence. Manual workflows collapse past a few hundred profiles; off-the-shelf tools don't fit the agency's workflow.
The solution
A queue-based architecture built from scratch. Tasks (scraping, analysis, reporting) move through BullMQ + Redis queues — processed reliably, nothing duplicated or lost, even on crashes. Apify-powered pipelines pull follower counts, engagement rates, posting patterns. A Groq LLM analytics layer generates insights — themes, audience quality, engagement. Real-time Slack notifications keep teams coordinated; CSV bulk import/export for ops; Bull Board for queue observability.
The outcome
Scales to 10,000+ profiles per campaign with observable queue processing. AI-driven insights replace manual content review. Team coordination automated through Slack.
Stack
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