AI Image Processing Platform

Context

Real-estate media has to look great and arrive fast. The challenge was doing both at volume: tens of thousands of images a day needing HDR processing, enhancement, and quality control — without costs scaling linearly or quality slipping.

Approach

I designed the platform as an event-driven pipeline. Work flows through queues, and stateless workers scale up and down with demand, so a spike in volume doesn’t require standing infrastructure the rest of the time.

Quality was treated as a first-class stage, not an afterthought:

• Elastic processing. SQS + Lambda let the system absorb bursts and idle cheaply, which is central to keeping per-image cost low.
• Automated QC with human-in-the-loop. Automated checks catch the common problems; a review stage handles the edge cases that matter for a customer-facing product.
• Webhooks push finished results into downstream systems so the pipeline integrates cleanly with the rest of the product.

Outcome

The platform reliably handles 100K+ images per day, with lower cost per image and faster turnaround — improvements that fed directly into customer satisfaction and the product’s growth.