Direct street-ready campaign imagery with the AI Street Poses Generator—direct it with clicks, not prompts.

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 gives you street-ready on-model visuals without waiting for samples, shipping schedules, or studio days. You can keep garment-led accuracy while directing pose, framing, lighting, and mood for the exact vibe your brand sells.

In RAWSHOT, street poses are built from the garment brief plus UI controls, and every export carries provenance signals. That means your merchandising workflow can publish consistently and iterate across variants in a predictable, budget-friendly way.

Because constant reshoots are expensive and slow, especially when you update colorways, trims, or seasonal variants. RAWSHOT lets you keep the same model settings and regenerate new street-pose imagery on demand.

Instead of restarting from scratch, you adjust the pose direction with buttons and presets while garment fidelity stays anchored to your product details. The result is fewer production bottlenecks and faster creative turnaround for ecommerce merchandising.

Upload your garments and choose your presentation controls in the RAWSHOT interface—pose preset, framing, and visual style—then generate the on-model output. There’s no text step; you steer the scene with camera, lighting, background, and crop options.

For teams, this makes it easier to standardize outputs across SKUs and maintain consistent street mood between product lines. You can also shift between 2K and 4K exports depending on where the images will be published.

Yes: garment-led control beats prompt roulette when you need consistent fashion results for PDPs. Generic image tools often rely on typed instructions, which can lead to garment drift, invented logos, and inconsistent faces across generations.

RAWSHOT is built as a real fashion application: UI controls direct the shoot, models can be saved for SKU consistency, and outputs include signed provenance and watermarking signals. That combination supports safer iteration for production workflows.

RAWSHOT outputs are labeled and include C2PA-signed provenance plus watermarking signals, so the production record travels with the file. For commercial teams, you also get full commercial rights to every output, permanent and worldwide.

This reduces licensing uncertainty that often shows up with generic image models. Your marketing and legal workflows can align faster because provenance and rights are explicit alongside each generated image.

Verify garment fidelity first: cut, color, pattern, logo placement, and drape should match the product brief. Then confirm framing for your use case—full body for campaigns, close-up for details—and check that the background and lighting match your brand style guide.

Finally, rely on the file’s signed provenance and watermarking cues to keep attribution clear. A quick review loop in RAWSHOT helps you avoid publishing outputs that fail your internal standards.

Photo generation is priced at about $0.55 per image with roughly 30–40 seconds per generation. Tokens never expire, and you can cancel in one click from the pricing page.

If a generation fails, the system refunds the tokens you spent. That keeps iteration predictable when you’re testing street poses across multiple variants.

Yes. RAWSHOT includes a REST API for catalog-scale pipelines, while also offering a browser GUI for single-shoot direction. You can keep the same engine and quality whether you’re iterating on a handful of SKUs or running a nightly batch.

For teams that manage large catalogs, this means consistent output patterns and fewer manual steps. The signed audit trail per image also supports operational traceability as content moves through your review process.

Your creative operators focus on selecting poses and visual styles in the browser, while production teams run repeatable batch jobs via the REST API. This separation is practical because the controls stay the same across UI and API payloads.

At scale, model saving helps keep the same face and body across SKUs, preventing inconsistencies that create rework. With per-image pricing, token refunds on failures, and clear rights per output, throughput stays reliable as your catalog grows.