Direct campaign-ready fashion imagery with clicks — powered by Velour 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 turns “reshoot the whole catalog” into “generate variants with locked controls.” You select camera, framing, pose, light, and visual style, and RAWSHOT keeps the garment as the brief so cut, color, pattern, and logo placement stay faithful across outputs.

When you scale, consistency matters: you can reuse the same model identity for SKUs to avoid drift. With C2PA-signed provenance and an audit trail per image, your publishing workflow stays auditable and ready for review.

Because season updates are predictable, and reshoots are not. RAWSHOT lets you generate on-model imagery per SKU using the same click-driven settings so the visual language stays stable while the product changes.

DIY prompting often introduces failure modes like garment drift or invented branding—your product mutates between attempts. With RAWSHOT, garment fidelity is the design center, and your output includes labelled provenance so your team can publish faster with fewer surprises.

You upload the real garment, then direct the shoot through in-app controls for lens, framing, pose, angle, lighting, background, mood, and visual style presets. RAWSHOT builds the output around the garment rather than bending it to match text instructions.

Once you have a look you like, you replicate it across your product line. The workflow supports single-shoot work in the browser and batch-scale generation via REST API, with C2PA-signed provenance attached per image for easier approvals.

Because the controls map directly to the decisions your merch team makes—framing, light quality, style direction, and product focus. With RAWSHOT, you click to direct the shoot, so iteration doesn’t depend on how well a model “interprets” your wording.

Generic image AI can create invented logos or inconsistent faces across outputs, which breaks catalog continuity. RAWSHOT focuses on garment fidelity and SKU consistency, plus it labels synthetic models so your team can publish with clear provenance.

Every RAWSHOT output comes with full commercial rights, permanent and worldwide. Outputs also include C2PA-signed provenance metadata and a signed audit trail per image, plus watermarking that supports transparent usage.

That means your compliance story is built into the file deliverable, not a post-process email thread. For teams shipping campaign and PDP content, you get clear labelling and traceability aligned to EU AI Act Article 50 and California SB 942.

Confirm garment fidelity first: cut, color, pattern, logo placement, and drape should match your real product. Then check continuity: faces and framing should remain consistent across your SKU set, so PDPs feel like one cohesive catalog.

Finally, keep provenance visible in your workflow. RAWSHOT includes C2PA-signed metadata and per-image audit trails, so you can verify what was generated and attach the correct files confidently before launch.

For photos, RAWSHOT prices around ~$0.55 per image with roughly ~30–40 seconds per generation, and tokens never expire. Failed generations refund tokens, and you can cancel with a one-click control on the pricing page.

Video uses more tokens per second than stills, so longer clips cost more. If your workload is primarily PDP and campaign selects, stills are the most direct way to build your on-model library quickly.

Yes. RAWSHOT supports browser GUI for single-shoot work and a REST API for catalog-scale pipelines, so teams can run batch generation alongside existing production processes.

Because the controls and outputs are consistent, you can apply the same visual direction to thousands of SKUs without prompt rework. You also keep an audit trail per image and signed provenance metadata to support approvals at scale.

A small team can move fast by splitting roles: one person directs the creative controls in the browser, while the pipeline runs batch generations for the rest of the catalog. SKU consistency and a stable model identity prevent the “re-shoot and re-approve” loop that slows launches.

As outputs arrive, provenance and watermarking make review smoother. With flat per-image pricing, token refund rules, and clear commercial rights, you can plan workload without per-seat gates or sales-call delays.