Direct campaign-ready fashion imagery, directed by clicks — with the Sherwani 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 turns fashion direction into repeatable settings you can reuse across SKUs. Instead of adjusting a new request every time, you lock lens, framing, lighting, and visual style, then regenerate sherwani imagery with consistent look and product-led fidelity.

This is built for commerce workflows: you can run a single browser shoot for approvals or push settings through the REST API for catalog-scale pipelines while keeping provenance and licensing clear on every output.

Because reshoots are slow, expensive, and hard to keep consistent across seasons. RAWSHOT focuses on garment fidelity and model consistency so your sherwani visuals stay coherent as you add or swap colors, trims, or patterns.

You direct the scene once using UI controls, generate at predictable timing, and keep outputs traceable with a signed audit trail per image—so changes move through approvals without redoing the entire production cycle.

You start by selecting product focus, framing, pose, and lighting in the RAWSHOT interface. Then you choose a visual style preset that matches your catalog tone, and generate the on-model result with your chosen aspect ratio and resolution.

Because every setting is a click, teams can standardize creative direction across lookbooks and PDP tiles while avoiding prompt-based drift that changes cut, color, or logo placement between outputs.

Prompt roulette asks the model to interpret your intent, which often leads to garment drift, invented details, and inconsistent product appearance. Garment-led control keeps the sherwani as the brief so cut, fabric drape, and pattern work stay anchored.

In practice, that means fewer reworks, clearer approvals, and more predictable output across batches—especially when you need consistent visuals across hundreds of SKUs.

You get C2PA-signed provenance metadata along with visible and cryptographic watermarking cues. That provides traceability you can use for governance, internal review, and publication workflows.

For commerce teams, the important part is operational: each generation includes a signed audit trail per image, and the output carries labelling so stakeholders understand what they’re licensing.

Start with garment fidelity: verify cut, color, pattern, and logo placement match your product. Then check model consistency across SKUs for the same styling line, and confirm framing meets your PDP or campaign requirements for the chosen aspect ratio.

Finally, ensure the output carries C2PA provenance and watermarking cues, since that’s what keeps approvals accountable and licensing straightforward across your production pipeline.

Photo generation is typically ~30–40 seconds per image at about ~$0.55 per image. Tokens never expire, and if a generation fails, the tokens are refunded—so daily uploads stay budgetable.

With predictable timing and flat per-image pricing, you can plan batch schedules for new drops, season refreshes, and ongoing catalog maintenance without the uncertainty of repeated prompt iterations.

Yes. RAWSHOT supports a REST API for catalog-scale pipelines, while the browser GUI covers single-shoot approvals and quick look exploration. You can reuse the same creative settings logic across both surfaces so the output stays consistent.

This also helps you keep auditability: every generated asset includes a signed audit trail per image, making it easier to connect outputs to approvals and downstream publishing steps.

One person can direct the initial creative direction in the GUI for approvals, then hand off the same settings approach to API-driven batch runs for ongoing updates. That keeps sherwani visuals consistent without needing a prompt engineer or an art director on every refresh.

Because core features aren’t locked behind per-seat gates, you can grow from single products to nightly catalog workflows while preserving provenance signalling and commercial rights framing across the team.