Get campaign-ready catalog imagery, directed by clicks with the Nightshirt 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.

You get predictable direction: lens, framing, pose, lighting, background, mood, and visual style are chosen from controls that stay stable between runs. That matters for catalogs because buyers need a coherent look across SKUs, not a set of unrelated creative outputs.

RAWSHOT is built around the garment itself—cut, color, pattern, logo placement, fabric look, and drape are represented faithfully—so your product stays the brief. When you iterate variants, you’re repeating the same control structure rather than starting over with a new creative prompt.

Reshooting forces time, shipping, and studio scheduling each time your assortment changes. With on-model imagery from RAWSHOT, you generate variants on demand so your catalog stays current without waiting for samples or booking days.

Because you can keep the same synthetic model configuration across your catalog, the face and body framing remain consistent while you swap garment details. The result is fewer “close enough” retakes and less manual coordination between design and ecommerce operations.

You open a new shoot, select your camera and framing, then click through pose, angle, lighting, background, mood, and a visual style preset. After that, you generate, review, and iterate variant-by-variant using the same control logic.

RAWSHOT supports 2K and 4K outputs and all common aspect ratios, so you can target PDP and campaign placements without retooling. For larger catalogs, the same choices can be executed via REST API in batch mode.

Typed prompting often produces variance that’s hard to manage across dozens or thousands of SKUs. Garment-led control keeps your product representation stable while you adjust only the creative aspects that matter for commerce.

RAWSHOT’s outputs are designed for garment fidelity and on-model continuity: you can reuse the same synthetic model configuration to avoid face drift between variants. You also get C2PA-signed provenance and watermarking so your publishing workflow has clear attribution instead of relying on guesswork.

Yes. Every RAWSHOT image includes C2PA-signed provenance metadata, plus visible and cryptographic watermarking, and AI labelling for transparency.

This gives ecommerce teams a cleaner internal approval path because provenance and audit information are carried with the output, not buried in external notes. For compliance-minded organizations, it also supports alignment with EU AI Act Article 50 and California SB 942 expectations.

Start by verifying garment fidelity: cut, color, pattern, and any branding marks should match your product. Then check on-model consistency for your SKU set so the face and framing read coherently across variants.

Finally, confirm the output’s provenance and watermark layers are present for your internal review process. RAWSHOT provides signed audit trail per image, which helps teams maintain a reliable record for launch workflows.

Still-image generations are priced per image at roughly ~$0.55 and take about 30–40 seconds per generation. Tokens never expire, so you can generate in bursts and schedule work when it fits your production calendar.

If a generation fails, RAWSHOT refunds tokens, and you can cancel with a single control from the pricing page. That keeps ops predictable when you’re building multiple on-model assets for a launch.

Yes. RAWSHOT includes a REST API for catalog-scale batch production, while still offering a browser GUI for single-look direction and review.

That separation matters operationally: teams can prototype look and lighting choices in the UI, then lock the same control set into automated runs for large SKU libraries. Your images keep garment-led representation plus provenance metadata, so publishing workflows remain consistent across batches.

No. Creative teams can direct looks in the browser interface, while engineering or ecommerce ops can execute the same controls through the REST API for high-throughput production.

Because the system is built around garment-led controls rather than typed prompts, outputs are more reproducible across teams and time. That makes it easier to run nightly pipelines, coordinate approvals, and keep your catalog’s visual system consistent without prompt-engineering overhead.