RenderBoard AI - Interior Design Workspace
Turn an interior plan into a reviewable render and presentation board without hiding cost, safety, or approval boundaries.
Cloud Services
GPU Workers
API Tests
Product Methods
The problem
Interior teams need to move from plans and references to client-ready options quickly, but AI rendering products often hide provider cost, moderation failure, asset provenance, and the point where a human must approve the result.
The challenge
I shaped the product around one supervised journey: upload a plan, confirm walls, rooms, and scale, place furniture and finishes, approve each room, then render and assemble a presentation board. Every paid action reserves credits before work starts and settles or refunds them when the job ends. Uploads are scanned and quarantined before anything reads them, and a final render cannot start until a person has approved the room. GPU work sleeps when idle and carries a hard time budget, so a stuck job cannot keep burning compute.
System Design
RenderBoard runs on Azure Container Apps: a Next.js web app, a FastAPI API with a malware-scanning sidecar, a plan-reading service, and Celery workers on Redis. GPU workers for path-traced rendering, upscaling, and segmentation scale to zero and wake from the queue. PostgreSQL enforces row-level security for the API, and files live in blob storage behind managed identity.
Results
RenderBoard is deployed end to end as an internal test release. A synthetic production journey has taken a plan from upload through room approval to a 4K path-traced render, and diffusion rendering, select-and-repaint, and upscaling are verified in the live environment. Payments stay off, and the public launch waits on the gates in the release register.
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Interested in this work?
I can walk through the architecture and code during an interview.