Direct your next product drop with the Wallet 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 how fast you can create consistent on-model imagery across many variants without losing control of the product. Instead of rebuilding a “look” every time, you reuse the same directed shot recipe and model settings while keeping garment-led fidelity.

RAWSHOT is built around the real garment: cut, color, pattern, logo placement, fabric, and drape stay faithful to your design. You also get C2PA-signed provenance plus watermarking cues, so outputs are publication-ready for commerce workflows.

Reshooting is slow, expensive, and hard to keep visually consistent when you have dozens or hundreds of product updates. A click-directed pipeline lets you refresh imagery quickly while preserving the exact framing and style you chose for the catalog.

With RAWSHOT, you can save a model and reuse it across your entire catalog to avoid face drift. Each generation includes signed provenance metadata and a per-image audit trail, which reduces review overhead when you publish at scale.

You direct the shot with UI controls: lens, framing, pose, lighting, background, mood, and a visual style preset. The software is engineered around the garment, so you’re selecting how the product is photographed rather than trying to “teach” a model via text.

This click-driven approach keeps your intent stable across batches, which is crucial for merchandising teams. You can generate 2K or 4K stills in multiple aspect ratios and export outputs with compliance-aligned provenance signals.

Because garment-led control reduces the biggest failures seen in DIY prompting: product mutation, invented branding, and inconsistent character across outputs. When you click the creative settings, you get repeatable merchandising results instead of rerolling until something “kind of works.”

RAWSHOT keeps the garment as the brief and preserves cut, color, pattern, and logo placement. It also supports SKU consistency by reusing the same synthetic model across your catalog, so variant pages stay cohesive.

Yes—outputs include C2PA-signed provenance, watermarking cues, and AI-labelled signals designed for transparent publishing workflows. You also receive a clear licensing story: full commercial rights to every output, permanent and worldwide.

For commerce teams, this means fewer internal debates about attribution or whether an image meets your publishing policy. The per-image audit trail helps your team review and manage batches with confidence.

Start by checking garment fidelity—cut, color, pattern, logo placement, and fabric/drape representation must match your design. Then confirm SKU consistency: the face and body should stay the same across variants when you reuse a saved model.

Finally, verify provenance and publishing cues. RAWSHOT outputs are C2PA-signed and include watermarking and labelling cues, backed by an audit trail per image so QA can sign off without guesswork.

For photo generation, pricing is predictable: about ~$0.55 per image with roughly 30–40 seconds per generation. Tokens never expire, and failed generations refund tokens, so your workflow doesn’t punish experimentation.

You can also cancel in one click from the pricing page. If you’re comparing against DIY iteration time, RAWSHOT keeps costs aligned to image production rather than prompt trial-and-error.

Yes. RAWSHOT includes a REST API designed for catalog-scale pipelines, while the browser GUI supports single-shoot direction for your creative team.

This matters for production because you can keep the same shot recipe and model consistency logic across nightly runs. Outputs include signed provenance metadata and an audit trail per image, which supports review and downstream publishing at scale.

Use the browser GUI to lock your shot recipe—lens, framing, lighting, visual style, background—and save the model you’ll reuse. Then move to catalog-scale generation using the REST API with the same directed settings across SKUs.

That progression keeps the team aligned on the creative standard while preserving product fidelity and provenance. It also protects your catalog from drift, so every variant stays consistent across your storefront updates.