Direct campaign-ready fashion visuals with the Fleece 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 token pricing, 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 on-model imagery into a repeatable workflow instead of a reshoot cycle. When you click the same camera, framing, lighting, and style presets for each SKU, your product visuals stay coherent while you move faster through assortments.

RAWSHOT also supports saved models for catalog consistency, carries C2PA-signed provenance metadata per image, and applies visible plus cryptographic watermarking with AI labelling. Use the REST API for batch generation so your catalog pipeline can stay deterministic and QA-friendly.

Because the bottleneck is rarely the garment; it is the schedule, the studio logistics, and the time it takes to produce consistent visuals across many variants. Click-driven direction lets you recreate the same brand photo language without booking new shoot days.

With RAWSHOT, you generate 2K and 4K outputs across aspect ratios, reuse saved models to avoid face drift, and publish with provenance that is C2PA-signed and watermark-backed. Season updates become controlled iterations rather than new production cycles.

You upload the garment and then direct the shoot using garment-led controls: lens choice, framing, pose, camera angle, lighting system, background, mood, and visual style presets. Each decision is a UI action, so your team can reproduce the same look across variants.

The system is designed around faithful garment representation—cut, color, pattern, logo placement, and fabric drape—so you do not spend time correcting product artifacts that would otherwise appear during manual prompt iteration. Generate, review, and export with signed provenance metadata and clear commercial rights.

Because prompt roulette creates unpredictable results, including garment drift and inconsistent product details across outputs. RAWSHOT keeps fashion-specific decisions inside structured controls so your outputs follow your creative direction consistently.

In practice, you click your camera and style presets, and you rely on RAWSHOT’s audit trail plus C2PA-signed provenance metadata per image before publishing. That workflow also reduces the risk of invented logos or mismatched faces that can show up when people iterate text-driven outputs for each SKU.

Yes. Every generated image includes AI-labelled output plus C2PA-signed provenance metadata, and it is protected with both visible and cryptographic watermarking.

This matters for commerce teams that need publish-ready assets with traceability, not just a pretty result. RAWSHOT’s compliance design aligns with EU AI Act Article 50 and California SB 942, and each image carries a signed audit trail you can use during internal QA.

Start with garment fidelity: verify cut, color, pattern, logo placement, and fabric drape match your real product. Then check framing and lighting for readability—especially on mobile aspect ratios—and confirm the model’s presence is consistent with your saved selection.

Finally, review the image’s provenance signals: C2PA-signed metadata, watermark cues, and the signed audit trail per output. RAWSHOT’s workflow is built to support that QA step so you can ship confidently without guessing where artifacts came from.

For photos, pricing is transparent: roughly ~$0.55 per image with about 30–40 seconds per generation. Tokens never expire, and you can cancel in one click on the pricing page if you need to stop a run.

If a generation fails, RAWSHOT refunds the tokens for that attempt. This makes batch testing practical for catalog teams who need multiple variants without worrying about silent overages.

Yes. RAWSHOT includes a REST API designed for catalog-scale pipelines, while the browser GUI supports single-shoot iterations and team approvals. You can generate in batches, apply your own QA gates, and keep the same garment-led control logic end to end.

Because outputs include signed provenance metadata and watermarking signals, your pipeline can treat attribution as a first-class field, not an afterthought. When you scale, use saved models to keep face and body consistent across SKUs.

Small teams can start in the browser GUI to lock in camera, framing, lighting, mood, and style direction, then move to nightly REST API runs once their review rules are clear. That shift keeps creative direction consistent while increasing output volume.

You also get predictable economics for photo generation and operational reliability through refund rules on failed outputs. The result is a workflow that stays manageable for roles like merchandisers, catalog producers, and designers—without prompt juggling or re-shoot scheduling.