01 — The challenge
Client challenge
aisr.qa needed a scalable way to deliver personalized book recommendations based on each user's job role and goals — without a manual curation process that couldn't scale to their growing user base.
Case study · Automation
AI book recommendation automation
A personalized recommendation workflow for aisr.qa that combines user job data with an AI interview. It returns eight role-relevant book picks, making recommendations feel curated without manual research.
Technology stack
- n8n
- OpenAI API
- Database Query
- Webhooks
02 — What I built
What I built
I built an n8n automation that queries the user database for professional context, conducts an AI-powered conversational interview to surface goals and challenges, and returns 8 curated book recommendations precisely matched to each user.
- Automated user data query from the database
- AI conversational interview pipeline via n8n
- Context-aware book recommendation generation
- 8 tailored recommendations per user per run
- Webhook-triggered delivery on user request
03 — The outcome
Measured outcomes
Every user receives fully personalized recommendations at scale with zero manual curation. The AI interview step grounds recommendations in each user's actual situation — going far beyond job title to deliver genuinely relevant results.
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