Direct your next drop’s campaign with the Clip 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 browser shoots and REST API payloads, which is why ecommerce teams can onboard quickly without rewriting creative briefs as chat threads.

For catalog teams, reliability matters more than model cleverness; RAWSHOT keeps token rules, generation timing, refund behavior, commercial-rights framing, provenance signalling, watermarking cues, and SKU-scale batch patterns explicit so operations can rehearse PDP launches without guessing.

It removes the bottleneck between “new garment ready” and “images ready,” so you can refresh PDPs and launch pages without reshooting every item. RAWSHOT is built around the product, so your cut, color, pattern, logo, and drape remain anchored to your garment setup.

You also get synthetic models labelled as such, plus C2PA-signed provenance and an audit trail per image. That’s the foundation for approvals, versioning, and consistent publishing across thousands of SKUs.

Because reshoots are slow, logistics-heavy, and expensive when you’re moving through colorways, new fabrics, and seasonal drops. RAWSHOT lets you generate publish-ready on-model imagery fast while keeping the garment faithful, so marketing can update faster than inventory cycles.

With click-driven controls, you can hold framing, lighting, and style steady across variant runs. Save and reuse a model to prevent identity drift between SKUs, so your catalog stays coherent.

In RAWSHOT, you click your way to the look: set product focus, framing, pose, camera angle, lighting, background, and a visual style preset. The system then generates on-model imagery that represents your garment details rather than bending the product to fit a typed request.

When you need multiple aspect ratios, you switch them in the UI and keep the same creative direction. Every output carries labelled provenance and watermarking cues so your QA process can treat generated assets like first-class files.

Garment-led control is stable: the garment is the brief, and your adjustments come from fixed controls that stay consistent between generations. Prompt-based workflows can introduce garment drift, invented branding, and silhouette changes that force you back into revisions.

RAWSHOT’s approach also supports SKU consistency by letting you reuse a saved model across your catalog. Combined with C2PA-signed provenance and audit trails, it’s easier to approve what you publish.

RAWSHOT outputs are AI-labelled and include C2PA-signed provenance plus visible and cryptographic watermarking cues. For commercial use, you receive full commercial rights to every output, permanent and worldwide.

That means fewer internal debates during marketing review, since the rights story and provenance data travel with the file. You can also integrate the workflow through GUI sampling and REST API batch production.

Start with garment fidelity: verify that cut, color, pattern, logo placement, and drape match your product spec. Then confirm likeness labelling and watermarking cues are present, since RAWSHOT includes labelled outputs and signed provenance for audit-ready files.

Finally, check consistency across variants—especially model face and body stability—before you approve a full catalog release. Save model settings and reuse them to keep SKUs coherent across launches.

For photos, pricing is per image—about ~$0.55—with typical generation time around ~30–40 seconds, and tokens never expire. Video uses more tokens per second than stills, so longer clips cost more, which is why teams budget differently for reels.

RAWSHOT also refunds tokens for failed generations, and you can cancel with one click on the pricing page. That keeps budgeting predictable when you run repeated variant tests.

Yes. RAWSHOT supports a browser GUI for single-look iterations and a REST API for catalog-scale pipelines, so you can keep the same creative controls across both workflows.

That makes it easier to plug generated assets into ecommerce systems and approvals, because the controls are explicit and reproducible. Every output includes signed provenance and an audit trail per image for operational traceability.

Operators can direct shoots quickly in the GUI for creative sampling, then production can switch to REST API batches for catalog volume without changing the underlying controls. Because model reuse supports SKU consistency, fewer teams need to manually reconcile inconsistent faces or drifting garment representations.

Publishers also get clearer asset handling thanks to C2PA-signed provenance, watermarking cues, and audit trails per image. The workflow stays practical: generate, verify, and publish with commercial rights and labelled transparency built in.