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.
Best for
Operations teams removing high-volume manual work
Business impact
A clearer, faster operating workflow
01 · The problem
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.
02 · The solution
What Orkestra built
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.
Key capabilities
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 results
Business impact
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.
04 · Stack used
The stack supports the workflow, reliability, and future maintenance requirements of the system.
Start a conversation
One focused conversation about the operation, the friction, and what a useful first release should change.
Direct founder access
No generic sales deck
A clear next step
