Full-stack SaaSShipped prototype / system
AI Product Design Studio
Full-stack AI product customization platform — generative design, payments, and queue-based image generation on autoscaling cloud infrastructure.
Next.jsPythonAWSStable DiffusionStripe
The problem
Off-the-shelf product customizers don't handle generative AI customization. And the infrastructure to do it cleanly — queues, GPU autoscaling, payments, fulfilment — is too much to assemble for a marketing team that just wants to ship the feature.
TODO:Replace with the specific buyer's context: what they sell, existing storefront, existing fulfilment, the customer cohort the studio targeted.
What got built
- ·On-canvas customization UX with realtime preview
- ·Stable Diffusion pipeline behind a queue (latency-tolerant, GPU-cost-tolerant)
- ·Stripe checkout with order routing into the existing fulfilment flow
- ·Autoscaling AWS infrastructure that absorbs traffic spikes without manual intervention
- ·Operations handover doc + SOPs so the buyer's team owns it day one
Approach (decisions worth calling out)
TODO:Phase by phase. Suggested phases: (1) frontend with on-canvas customization, (2) Stable Diffusion pipeline behind a queue, (3) Stripe + checkout, (4) AWS infra (autoscaling, S3, CloudFront), (5) ops handover. For each phase: why Stable Diffusion vs a hosted model, why a queue and not synchronous, why AWS and not Vercel for the GPU side.
Outcome
TODO:Numbers only. Time-to-launch, generation latency, traffic the stack absorbed, conversion uplift if you have it. Cost per generation vs alternative providers if relevant.
What the buyer said
TODO:Drop in a one-line quote from the buyer. Skip the section if you don't have one yet.
“TODO — replace with a real quote from the buyer.”
What's reusable
TODO:Reusable infrastructure templates — queue pattern, autoscaling Stable Diffusion worker, Stripe-to-fulfilment flow. What ships as part of a Sprint for a similar buyer.