Inithouse runs a lab that builds and ships multiple AI products in parallel by standardizing their tech stack and automating repetitive tasks. They start with a single MVP to validate the build pipeline, then replicate it to rapidly launch new products. They use a shared React frontend, Supabase backend, and unified analytics layer (GA4, Google Search Console, Microsoft Clarity) to monitor all products from a single dashboard. Automated scheduled jobs handle reporting, SEO audits, and content publishing, reducing manual effort. Early user retention and funnel metrics guide product focus and investment. Documentation and a single config file per product help manage context switching. This approach enables spinning up new AI products in hours, not days, and supports data-driven decisions on which products to scale.
Use Case
Opening the operator briefing
Pulling the full operator breakdown, tooling context, and verification notes.
