Direct campaign-ready hanbok imagery with clicks — with the Hanbok AI On-model Photography Generator.

Never—you direct every output with sliders, presets, and clicks on the garment, not typed prompts. That UI control is consistent across GUI and REST API payloads, which is why ecommerce teams onboard buyers without rewriting creative briefs as chat threads.

For catalog teams, reliability matters more than model cleverness; RAWSHOT keeps tokens, timings, refund rules, commercial rights framing, provenance signalling, watermarking cues, REST surface, and SKU-scale batch patterns explicit so operations can rehearse PDP launches without hallucinated garment inventions.

It changes who can produce consistent on-model visuals at catalog speed. Instead of booking studio days for each season update, you generate new campaign-ready imagery directly from your product inputs while keeping creative direction inside the RAWSHOT controls.

For ecommerce teams, the practical win is repeatability: you can save a synthetic model and reuse it across your SKU set so faces and bodies stay aligned, while garment-led fidelity stays anchored to cut, colour, pattern, logo, fabric, and drape.

Because teams shouldn’t be blocked by scheduling, samples, and reshoot costs every time your catalogue changes. With RAWSHOT, you can generate additional on-model visuals from your garments and keep a consistent visual system across the season.

The workflow stays click-driven: set camera, framing, pose, lighting, background, and visual style presets, then generate. The output includes signed provenance metadata and watermarking cues, so publishing is operationally cleaner for commerce pipelines.

You start a new shoot and choose the photographic setup from the interface. Every setting—from lens and framing to mood, lighting, and aspect ratio—is a control you click and adjust rather than a text instruction you write.

RAWSHOT is engineered around the garment, so the product stays faithful while the scene adapts. Each image carries signed audit trail and labels, letting your team publish with a clearer compliance story than untracked DIY outputs.

Because fashion PDPs need consistency, not unpredictability. Prompt-driven workflows often lead to garment drift, invented logos, or inconsistent faces between generations, forcing teams back into iteration loops.

With RAWSHOT, your product fidelity is guarded by a garment-led setup and UI controls that keep the creative direction structured. You also get synthetic model labels, C2PA provenance, and a stable production path that works the same in the browser GUI and REST API.

Every RAWSHOT output ships with full commercial rights, permanent and worldwide. That means your marketing, ecommerce, marketplace listings, and repeat seasonal campaigns don’t need a messy rights interpretation step after generation.

On top of rights clarity, outputs include C2PA-signed provenance and watermarking cues plus AI-labelling for transparency. This helps commerce teams align publishing practices with compliance expectations while keeping the pipeline practical.

Check garment fidelity, model consistency, and provenance signalling. RAWSHOT’s proof surfaces are designed for that QA mindset: the garment-led controls keep cut, colour, pattern, logo, fabric, and drape faithful, and synthetic models are transparently labelled.

Then verify the output carries C2PA-signed provenance, visible and cryptographic watermarking, and a signed audit trail per image. Those signals are built for teams who need confidence before images hit PDPs or paid placements.

For stills, the cost is transparent and predictable: about ~$0.55 per image with roughly ~30–40 seconds per generation. Tokens never expire, and failed generations refund tokens, which matters when you’re iterating across variants.

Video and model workflows cost more because they use more tokens, but the operational model stays the same. You also get a one-click cancel path on the pricing page so teams can control spend during testing.

Yes. RAWSHOT provides a REST API path for catalog-scale generation while the browser GUI supports single-shoot work. That lets the same garment-led control logic run across small teams and large SKU pipelines.

Because each output carries signed provenance and watermarking, your automated publishing pipeline can stay compliant and auditable. You also keep model consistency across SKUs by saving and reusing the synthetic model where needed.

Start with the browser GUI to define your visual system—lens, framing, lighting, background, mood, and style presets—then move those choices into batch production with the REST API. This gives you a repeatable creative recipe that your team can run on demand or nightly.

Operationally, you keep consistency by reusing saved models across SKUs, which prevents face/body drift and reduces retakes. Each batch output includes signed provenance and clear commercial rights, so publishing remains stable as volume grows.