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
Built
Queue-based AI analytics backend
Applied AI
Reliable, observable, queue-driven AI analytics at scale.
10,000+profiles / campaign
overdrive/pipeline
10,000+
✓queue · 10,000+ profiles enqueued (BullMQ)
✓ingest · Apify pull complete
✓analyze · Groq LLM insights generated
▸notify · Slack digest dispatched
The problem
What was broken
Influencer campaigns require analyzing thousands of profiles. Manual workflows collapse at scale.
The solution
What we built
A queue-based architecture (BullMQ + Redis) pulling data via Apify and generating insights with a Groq LLM layer.
The outcome
What shipped
Scales to 10,000+ profiles reliably. AI-driven insights replace manual review.
Built with
Node.jsTypeScriptMySQLBullMQRedisApifyGroq LLMAWS S3Slack APIBull Board
Reflects designed capacity from the build; live numbers verified per engagement.
Next case study
Truth Signal
A no-functionality-loss serverless re-architecture engagement.
Let's build something that ships.
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