Direct your next drop with the AI Outfit Generator—campaign-ready fashion imagery directed by clicks.

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 turns fashion photography into a repeatable production step instead of a one-time studio event. You select your garment, choose the look, and generate stills at 2K or 4K without relying on prompt roulette. The result is consistent on-model presentation that you can attach to multiple product variants and publish faster.

With RAWSHOT, every creative decision stays inside the interface: camera, framing, lighting, background, mood, and visual style are tuned as controls. That keeps garment fidelity tied to your real product and reduces the rework that normally comes from drift between outputs.

Because reshoots reset your timeline, your approvals, and your consistency across a full catalog. When you update season-by-season, garments need new imagery—but you also need stable models, stable look direction, and stable product details. RAWSHOT is built for those constraints, not just for making an image.

You can generate campaign or catalog-ready stills with the same UI controls and the same model presentation across SKUs, avoiding face changes and product mutation between generations. Each output includes provenance metadata and labelling cues so your publishing flow stays auditable and consistent.

You start in the browser GUI by selecting the garment and a style preset, then you direct the shoot with controls for lens, framing, pose, lighting, and background. RAWSHOT replaces the prompt box with a real application workflow so decisions are made as clicks, not text.

Once the composition matches your catalog requirements, you generate and review in the same interface. The garment remains the brief—cut, color, pattern, logo, and drape stay faithful as you iterate across angles, aspects, and compositions.

Prompt-based workflows often optimize for what the model thinks is a vibe, not for preserving your exact product details. That’s why DIY prompting can cause garment drift, invented logos, or inconsistent faces across outputs, which creates expensive downstream corrections for ecommerce teams.

RAWSHOT keeps garment fidelity grounded in the actual product you select and provides consistent model settings across SKU batches. It also includes provenance metadata and labelled outputs so your team can publish without guessing what was generated or how to explain it internally.

Yes. RAWSHOT outputs come with C2PA-signed provenance metadata and watermarking, and the synthetic models are transparently labelled so teams can maintain clarity during approvals. That gives you a clean commercial narrative beyond “it looks good.”

On licensing, you receive full commercial rights to every output for permanent, worldwide use. In practice, this means you can build PDPs, lookbooks, and ads while keeping compliance and provenance cues inside the deliverables your team already processes.

Start with garment fidelity: confirm cut, color, pattern, logo, and fabric drape match your real product. Then verify consistency: use the same model settings across related SKUs so faces and body presentation don’t change between variants. Finally, confirm that the output includes provenance signalling and watermarking cues your team expects for publishing.

In RAWSHOT, these checks align with how the controls are designed—camera and style adjustments are explicit, not inferred from text. That reduces the risk of last-minute corrections that typically come from drift in generic image generation.

For photos, pricing is per image at about ~$0.55, and generation typically takes ~30–40 seconds per result. Tokens never expire, and the cancel button is available on the pricing page. If a generation fails, tokens are refunded so your workflow doesn’t get stuck on unpredictable runs.

Video uses more tokens per second than stills, so longer clips cost more. The operational takeaway is to choose stills for SKU iteration loops and reserve video for launches where motion adds conversion value.

Yes. RAWSHOT supports catalog-scale workflows with a REST API alongside the browser GUI. You can use the same product-led controls and generate outputs in batch for multiple SKUs without rebuilding creative direction in separate systems.

This matters because ecommerce teams often have approvals, naming, and ingest pipelines that already exist. With RAWSHOT, the deliverables come with provenance metadata and labelling cues so your automation can remain audit-ready while your catalog expands.

The GUI is optimized for single-shoot direction—when you want to dial in a look quickly, review it, and generate a small set for immediate publishing. Catalog scale focuses on throughput: batch generation across SKUs with consistent model settings and controlled variations, without per-seat gating.

If you’re building a night pipeline or updating a large catalog, the REST API keeps your workflow consistent. If you’re styling a campaign drop, the browser interface gives you fast iteration with the same garment-led approach, provenance signalling, and commercial rights framing.