1. Generation is the easy part

Our photography pipeline is diffusion-based and conditioned on real garment references. The creative layer (scene, pose, lighting, model) is exposed to users as presets in a self-service studio: a library of scenes, models, poses and complete campaign looks. That choice is deliberate. Our users are fashion brands, retailers, designers and agencies, not prompt engineers. Presets make outputs reproducible across a collection, which matters when a lookbook must look consistent from the first look to the last, and they remove the need for any AI or prompt knowledge.

2. Automatic QA with vision-language models

Every generated photo goes through an automated QA pass. A vision-language model compares the output with the source garment and inspects it for product consistency and image quality: does the silhouette match, are color and print preserved, are construction details where they should be, is the image free of typical generative artifacts. Images that fail are flagged for regeneration or refinement instead of reaching the client. The point is a fine-tooth comb applied to every single photo, which is impossible to do manually at the scale of collections with thousands of images.

3. The refinement bot: inpainting what used to be manual

Some defects are not worth a full regeneration. Labels, trims and hardware, embroidered logos and fabric textures are the classic ones, and historically they were fixed 100% by human retouchers. Our refinement bot handles these with region-masked inpainting: it isolates the affected region, swaps or corrects the label or trim, restores embroidered logos and reconstructs fabric texture while leaving the rest of the image untouched. For enterprise brands, a service layer of fashion, photography, design and AI experts sits on top, so what is delivered is ready to go live.

4. Digital twins and LoRA identity fidelity

Brands often want a recognizable face, and real models want to keep working. We build digital twins of real human models: each model gets a LoRA fine-tuned on more than 100 photos of that person, which gives the identity fidelity needed to generate any pose, angle or setting while remaining recognizably her. On top of the twins library we run a casting and royalties module. Each generated image is attributed to the twin used, and the model is paid per photo. Think Spotify for models: a model can scale her image across more and more iconic brands and earn more than with traditional in-person shoots.

5. Fashion films: selection over generation

Video models still produce temporal anomalies: limbs that morph, fabric that flickers, hands that glitch. Instead of hoping for a perfect take, we generate multiple takes and let the system itself select the best segments across generations, discarding anomalous ones, then assemble them into longer, already edited fashion films.

6. Real-time virtual try-on

Our try-on runs in real time on video through the shopper's camera. Pose and biometric body tracking keep the garment synchronized with the body as the shopper moves. Aesthetic try-on answers "does this look good on me"; it is combined with a smart size finder that answers "which size fits me", the two questions behind most fashion returns.

7. GEO: making assets readable by AI assistants

Shoppers increasingly ask AI assistants what to buy. Generative Engine Optimization is the practice of making content legible to those systems. We embed machine-readable metadata into photos, videos and content (XMP/IPTC fields in the files, schema.org Product markup on the page, provenance in the style of C2PA) that describes the visual context of what is shown in each image (garment, color, fabric, fit, model, scene, styling and occasion) and ties every asset back to the direct product page link, so that the major AIs can recommend the PDP.

8. Live commerce with AI avatars

The last module takes a single live source and multistreams it (RTMP fan-out) simultaneously to TikTok, Shopee, Instagram and SHEIN. Products are presented by 100% AI avatars in AI-generated scenes. Comment bots answer every viewer in real time across channels and manage carts during the live, which is the part human hosts cannot scale.

Takeaways

• In vertical gen-AI, QA is the product.
• Presets beat prompts for non-technical users.
• Targeted inpainting is cheaper and safer than regeneration.
• Aligning incentives with real models (royalties) turns a supply risk into a moat.

Learn more at https://futuriza.io/en. Felipe Corrêa da Silva, Founder & CEO.