AI onboarding module.
An onboarding module that researched new customers and tailored the opening experience based on what it found, built during RevGeni's early stage.
the problem
Generic SaaS onboarding ignores the customer. Every new sign-up sees the same tour, the same empty dashboard, the same 'pick your first template' screen. For a product aimed at helping B2B SMBs grow without scaling headcount, that miss was expensive. RevGeni needed onboarding that actually met each customer with something specific to them on day one.
the approach
Built the module to research the incoming customer using public data, then use that research to shape the onboarding flow. Started as a prototype in n8n to prove the research-plus-personalization loop, then got refactored into the RevGeni platform once the pattern held. Claude Code handled the implementation refactor from prototype to production, alongside the LLM chains doing the actual research and personalization work. Built solo with product direction from the founder.
the outcome
Shipped into the platform and became the first thing every new customer hit. Cited specifically in a public recommendation from the founder as something customers continued to reference after signup. Early validation that researching upstream of the product experience was worth the complexity.
the lessons
Personalized onboarding is a different engineering problem from personalized content generation. The research step has to be fast, bounded, and willing to return 'I do not know enough' gracefully. A confident wrong personalization is worse than a generic one.
stack
- n8n ·
- Claude Code ·
- LLM chains