Photograph kaftans for your next drop with the Kaftan AI On-model Photography Generator—directed by clicks, not prompts.

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 stop reshooting the same kaftan for every variant and start generating consistent on-model imagery from a repeatable control set. Instead of juggling a new creative workaround each time a colorway changes, you adjust UI settings while keeping the garment as the brief and the synthetic model consistent.

That matters for commerce because customers notice inconsistency across PDPs. With RAWSHOT, your team gets labelled outputs with C2PA-signed provenance and an audit trail, so publishing stays operationally clean even when you move fast.

Because SKU updates often become a scheduling problem, not a creative problem. Traditional shoots demand studio time, sample shipping, and rebookings for small changes like mood lighting, background, or visual style.

RAWSHOT keeps those changes inside the interface—select a new background, lighting system, and campaign/editorial preset, then generate. Your outputs also carry provenance metadata and watermarks, so you can route approvals and licensing with less friction than untracked DIY workflows.

In RAWSHOT, you click the camera and direction controls: lens, framing, pose, camera angle, lighting, background, mood, and a visual style preset. You’re not writing a description—every choice is an operational input tied to the photo output.

The garment is represented faithfully, including cut, color, pattern, logo, fabric, and drape, so you’re steering the shoot without asking the system to invent missing details. When you hit generate, you also get labelled, watermarked outputs with a signed audit trail per image.

DIY prompting asks you to engineer language and accept variability, which is a poor fit for fashion commerce where the garment must stay consistent across variants. Generic models can drift on fabric texture, invent branding, or change the model face between outputs, so you spend time selecting the “closest” result.

RAWSHOT replaces that with click-driven garment-led controls and SKU consistency. You also get C2PA-signed provenance, watermarking cues, and full commercial rights language that your team can apply across catalog and campaign workflows.

RAWSHOT outputs are labelled and include C2PA-signed provenance metadata, with visible and cryptographic watermarking cues. Every generated image carries a signed audit trail per image, which helps compliance and marketing teams understand what was created and why it’s safe to publish.

This isn’t a “trust us” posture—your operations get explicit attribution signals and labelling so your review process becomes consistent across batches. You can move kaftan imagery from draft to PDP and campaign placements without scrambling for documentation.

Run a garment fidelity check first: confirm cut, color, pattern, logo presence, and fabric drape match your product files. Then verify identity consistency if you’re running multiple SKUs, and make sure lighting, background, and framing align with the layout rules of your PDP templates.

With RAWSHOT, you can also verify provenance and labelling cues since outputs are C2PA-signed and watermarked with an audit trail per image. That gives you a reliable pre-publish checklist that doesn’t rely on comparing random DIY generations.

For stills, budget around ~$0.55 per image, with about 30–40 seconds per generation and tokens that never expire. Costs are tied to the output you request, not to seats or a “talk to sales” approval process.

If you also generate video, remember it costs more per second because it uses more tokens per second than stills. RAWSHOT also refunds tokens for failed generations, and you can cancel in one click when timing changes.

Yes. RAWSHOT provides a REST API for catalog-scale pipelines, while also offering a browser GUI for single shoots and quick creative iterations. That means the same garment-led generation engine powers both ad hoc requests and nightly SKU batches.

In practice, you run generation jobs based on your product list, then consume labelled, watermarked outputs back into your catalog publishing workflow. With signed audit trail metadata, your teams can keep approval processes predictable even when outputs arrive in bulk.

Throughput depends on how many image jobs you queue, not on a per-seat gate or a plan that throttles growth. RAWSHOT keeps the same core engine and output quality for browser GUI work and REST API pipelines, so multiple operators can work in parallel with predictable per-image pricing.

For teams, the main operational win is consistency: same model face across SKUs, click-driven direction for repeatable lighting and framing, and explicit provenance metadata for publishing. That lets you scale kaftan imagery without turning approvals into a guessing game.