Automating Product Imagery, Saving Millions In Production Costs.

A Dubai luxury sculpture studio had more than a thousand high-value products to market, many selling for $30K–$40K. Buyers needed to see those pieces inside real architectural environments — villas, hotels, courtyards, spas, commercial spaces — not isolated against white backgrounds.

Traditionally, producing that imagery meant manufacturing or preparing the physical product, transporting and installing it, securing premium locations, styling the environment, hiring a photography crew, shooting multiple views and retouching the assets. At catalogue scale, product marketing became a multi-million-dollar physical-production problem.

I was brought in to determine whether generative AI could replace enough of that workflow to make catalogue production commercially scalable. What started as a ten-product experiment became a production system combining LLM-based planning, multimodal evaluation, catalogue retrieval, client-preference memory, product and scene guardrails, confidence-based routing, human review, automated QA and publishing.

The result was not an image generator. It became an AI photography and interior-design production workflow.

A sculpture can only be understood properly when its scale, material and placement are visible in context. For every product the client needed several views: scale in a real room, architectural placement, material under natural and artificial light, alternate angles, close detail, and day against evening.

At roughly seven useful views per product, a catalogue of 1,000+ products implied more than 7,000 finished marketing images. Producing those conventionally meant repeating an expensive physical process for each one — fabrication, transport, installation, location, styling, photography, retouching.

The client needed high-quality environmental imagery to sell the products, and could not justify physically staging every product purely to create it.

Could I recreate enough of the photography and interior-design workflow in software without compromising the physical truth of the product?

I proposed a workflow where the studio provides a product reference — a sketch, render or source image — and the system handles the rest of the visual production process.

For each product the system needed to understand the product and its category, retrieve catalogue and preference context, design an appropriate environment, determine realistic placement, choose materials and lighting, generate one representative scene, evaluate it, preserve the approved room and product across views, generate the remaining shot family, and package the outputs for ecommerce use.

• Interior design agent — plans the environment before anything renders.
• Image generation model — executes the brief.
• Multimodal evaluator — scores fidelity, placement, room quality and preference match.
• Catalogue and preference retrieval — supplies approved and rejected context.
• Production harness — owns guardrails, retries, thresholds, routing and publishing.

The first goal was not full automation. It was reliable, client-acceptable output.

The initial ten-product pilot looked promising, but client review exposed four failure classes.

Scaling to a hundred products revealed another problem: catalogue-level repetition. At a hundred products and roughly seven images each, a rejected concept could invalidate an entire seven-image family. With early concept approval around 50%, hundreds of finished images could be produced with no commercial value.

I needed to move judgement earlier than generation cost.

The system was not designed to eliminate humans. It was designed to use them where their judgement produced the most value.

The project evolved through distinct stages, each one exposing the constraint that defined the next.

• ~10 products — manual pilot.
• ~100 products — early automation, roughly 50% approval.
• Another ~100 — with prompt and image evals, roughly 75–80% approval.
• Human-reviewed first images — near-perfect approval, but too much manual review.
• ~500 products — confidence-routed, roughly 95% approval with around 90% less client review.
• Architecture designed toward ~2,000-product batches.

Most importantly, the studio no longer needed to physically manufacture and stage every catalogue item purely to create environmental marketing imagery.

The value was not cheaper image generation. It was marketing a much larger catalogue without physical-production cost rising linearly with catalogue size.

• LLMs for interpretation, planning and prompt generation.
• Multimodal evals for product fidelity, placement, room quality and consistency.
• Retrieval and preference memory over approved and rejected references.
• Guardrails for hard constraints.
• Evals placed before expensive generation.
• Canonical scene state for consistency across a shot family.
• Confidence-based human-in-the-loop routing.
• Orchestration for retries, thresholds, QA and publishing.

The shape of the work was: business problem, prototype, deployment failure, system redesign, client feedback, scaling, operational impact. Each stage was defined by the constraint the previous one exposed.

This was never about finding the perfect model. It was about building enough structure around imperfect models that they could reliably do real client work.