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Rawshot.ai

Catalog · Motion-ready · 4K options

Direct your next drop with the AI Catalog Video Generator.

Generate on-model motion reels from your actual garments—no prompts, no studio days. Click a scene builder, lock the camera style, then adjust framing and action with sliders and presets. You don’t need to become a prompt engineer; you direct the shoot with controls.

  • ~$0.22 per second · video
  • ~50–60s per generation
  • Locked scene builder
  • 4K-ready output
  • Tokens never expire
  • Full commercial rights

7-day free trial • 50 tokens (10 images) • Cancel anytime

Try it — every setting is a click
2:3 · 720p
1 scenes4s

Block the scene. Zero prompts.

Pick a locked camera start, choose framing and lighting, then set model action and duration. Every setting is a click, slider, or preset—garments stay the brief from first frame to final reel. ~4s clip · locked camera

  • 6 clicks · 0 keystrokes
  • app.rawshot.ai / build_scene
Video Builder
app.rawshot.ai / build_scene
Shot count
Framing
Duration (sec)
34s10
Lighting
Background
Resolution
Aspect ratio
Model action
Camera motion
1 scenes · 4s · Static locked
Generate reel

How it works

Click-driven reels for catalog pipelines

Scene builder presets set the look. You adjust motion, action, and framing with sliders—then generate labeled output for your catalog release.

  1. Step 01

    Choose a motion scene

    Open the scene builder, select camera motion, framing, and lighting from presets. You’re directing the reel with controls that map to real production decisions.

  2. Step 02

    Set action and duration

    Pick model action, then set clip length and aspect ratio for your platform. No prompt text—your garment-led setup stays consistent shot to shot.

  3. Step 03

    Generate, label, and ship

    Generate the reel, then review the labeled output with provenance metadata and watermarking cues. Export with full commercial rights and a permanent record for your catalog workflow.

Spec sheet

Twelve proofs for garment-led video

Each tile validates a different requirement: garment fidelity, model consistency, provenance, compliance, scale controls, and publish-ready commercial rights.

  1. 01

    No-likeness by design

    Synthetic models are built from 28 body attributes with 10+ options each, and accidental real-person likeness is statistically negligible by design. Every reel stays on the synthetic model track, transparently labeled.

  2. 02

    Click-driven controls, zero prompting

    Every creative choice is a button, slider, or preset: camera motion, framing, action, lighting, and background. You never type a command; you direct the shoot with an application interface.

  3. 03

    Garment fidelity stays true

    Cut, colour, pattern, logo placement, fabric behavior, and drape are represented faithfully. The garment is the brief, so your reel matches the product you plan to sell.

  4. 04

    Synthetic model diversity

    You get diverse synthetic models that are transparently labelled as synthetic composites. That means consistent look and coverage without relying on real-person casting for every SKU.

  5. 05

    SKU consistency, no drift

    Use the same model setup across your catalog so faces and body framing remain stable. That removes retakes and reduces variance between variants and updates.

  6. 06

    150+ visual styles

    Switch between catalog, lifestyle, editorial, campaign, studio, street, and more using style presets. Keep your brand’s visual language while changing the reel’s mood.

  7. 07

    Resolution and aspect ratio coverage

    Generate at 2K or 4K and select every aspect ratio you need. Your reels can be repurposed across feeds while keeping a consistent brand-grade finish.

  8. 08

    Compliance and provenance signals

    Outputs come with C2PA-signed provenance, including compliance alignment for EU AI Act Article 50 and California SB 942. Labeling is part of the product experience, not an afterthought.

  9. 09

    Signed audit trail per image

    Each output carries a signed audit trail so teams can verify what was generated. That supports internal QA, publishing review, and catalog governance.

  10. 10

    GUI for single shoots + REST API

    Work in the browser for individual looks, then scale the same workflow via REST API for nightly pipelines. Your catalog team can automate reel generation without losing control.

  11. 11

    Fast per-reel economics

    Video generation uses ~$0.22 per second and requires more tokens per second than stills. Clips are generated in about 50–60 seconds, and tokens never expire.

  12. 12

    Full commercial rights worldwide

    Every output includes full commercial rights, permanent, worldwide. You can publish and reuse reels as part of your product marketing without an unclear licensing story.

Outputs

Reel gallery that’s ready to publish Labeled, watermarked, controlled

Preview a set of motion outputs built from the same garment setup, then export for your catalog or campaign channels.

Watermarked reel export
Provenance-signed output
Platform-ready aspect variant

Browse 150+ visual styles →

Comparison

RAWSHOT vs category tools vs DIY prompting

Three lenses on every dimension — what you optimize for in RAWSHOT versus typical category tools and blank-box AI workflows.

  1. 01

    Interface

    RAWSHOT

    Click-driven scene building with sliders and presets for every production choice.

    Category tools + DIY

    Prompt-first or limited controls that behave like a text interface with fashion shortcuts. DIY prompting: Typed prompts require prompt iteration before you get usable results.
  2. 02

    Garment fidelity

    RAWSHOT

    Garment-led generation preserves cut, colour, pattern, logo, fabric, and drape.

    Category tools + DIY

    Prompt influence can bend garments, causing wardrobe details to drift between outputs. DIY prompting: DIY methods often mutate the product details and shape over repeated generations.
  3. 03

    Model consistency across SKUs

    RAWSHOT

    Stable model setup reduces cross-SKU variance so variants feel catalog-consistent.

    Category tools + DIY

    Model and face changes are common across generations; consistency is not guaranteed. DIY prompting: Faces and body framing can vary every run, creating inconsistent catalog results.
  4. 04

    Provenance + labelling

    RAWSHOT

    C2PA-signed provenance plus AI labeling and watermarking cues are included in outputs.

    Category tools + DIY

    Often lacks signed provenance or clear labeling for downstream workflows. DIY prompting: DIY output typically has no C2PA record, no consistent labelling, and no audit trail.
  5. 05

    Commercial rights

    RAWSHOT

    Full commercial rights to every output, permanent and worldwide.

    Category tools + DIY

    Rights terms can be unclear or gated by per-seat and per-tier rules. DIY prompting: Rights and reuse clarity is harder to maintain when outputs vary and metadata is missing.
  6. 06

    Iteration speed

    RAWSHOT

    Adjust with controls, then re-generate with predictable timing and clear costs.

    Category tools + DIY

    Iterations can be slower due to weaker controls and higher variance in outputs. DIY prompting: Prompt-engineering overhead delays iteration and increases wasted generations.
  7. 07

    Pricing transparency

    RAWSHOT

    Simple per-output economics: ~$0.22 per second for video, tokens never expire.

    Category tools + DIY

    Per-seat pricing and volume tiers often complicate budgeting as catalog scale grows. DIY prompting: Hidden iteration costs stack quickly while waiting for a prompt that finally works.
  8. 08

    Catalog API

    RAWSHOT

    GUI for single reels plus REST API for batch pipelines and nightly production runs.

    Category tools + DIY

    Catalog automation is often limited or requires nonstandard integrations and workarounds. DIY prompting: DIY pipelines are harder to automate reliably because prompt text becomes the control layer.

Prompting does not scale

Stop writing essays. Direct the shoot.

Most AI photo tools start with a blank text box. Rawshot turns the shoot into repeatable controls, so creative teams can produce consistent fashion imagery without prompt syntax or one-off hacks.

Category norm

Manual
Prompt box

Create a premium editorial fashion photograph of a model wearing the exact navy oversized wool coat from SKU-1842, full-body crop, realistic hands, consistent facial identity, clean e-commerce lighting, subtle Paris street background, 85mm lens, no logo distortion, no fabric hallucination, same pose as last campaign, repeatable for all colorways...

Needs prompt engineering
Breaks across SKUs
Hard to repeat

A prompt can describe one image. It cannot become a shared production system for hundreds of products, models, angles and markets.

Rawshot

Clicks

Saved shoot recipe

Apply to 1 SKU or 10,000 via GUI, CSV or REST API.

Scale
Preset-driven shoots anyone can repeat
Same model, pose and styling across a catalog
GUI for teams, API for production volume

Rawshot makes creative direction visible: buttons, presets and sliders instead of hidden prompt craft. The result is easier to teach, faster to approve and built for repeat production.

Use cases

Catalog motion reels for every operator

Operator archetypes and how click-directed, garment-first output fits the way they actually work.

  1. 01

    Indie designer launching a lookbook reel

    You build one motion direction, then generate coordinated reels across product variants without reshooting or retelling creative specs.

    Confidence · high

  2. 02

    DTC brand updating seasonal drops

    When the same garment line shifts, you keep visual consistency while producing new aspect ratios and backgrounds for the next campaign.

    Confidence · high

  3. 03

    Ecommerce catalog team scaling SKU reels

    You run nightly pipelines with the REST API so each SKU receives publish-ready motion clips without cross-output drift.

    Confidence · high

  4. 04

    Influencer brand manager maintaining a consistent face

    You reuse the same model setup so every Reel feels aligned across platforms, while still varying framing and lighting by preset.

    Confidence · high

  5. 05

    Adaptive fashion line with accessible production workflows

    You generate reels without sample shipping, keeping garment fidelity as the brief while maintaining consistent visuals across catalog updates.

    Confidence · high

  6. 06

    Lingerie DTC product marketing

    You control framing and detail shots for product-first reels while preserving garment cut and drape for accurate representation.

    Confidence · high

  7. 07

    Resale and vintage marketplace listings

    You standardize motion across reused product categories, reducing variance so customer browsing feels coherent across listings.

    Confidence · high

  8. 08

    Factory-direct manufacturer for multi-SKU approvals

    You produce motion outputs with a signed audit trail so approvals move faster and approvals are repeatable across SKUs.

    Confidence · high

  9. 09

    Kidswear label producing platform-specific reels

    You generate consistent motion scenes and aspect ratios for the channels you publish on, without prompt overhead between updates.

    Confidence · high

  10. 10

    Jewelry and accessory brand detail reels

    You focus on close-up and detail framings, switching styles while keeping the garment brief precise for each product.

    Confidence · high

  11. 11

    Student fashion team building a portfolio at scale

    You create editorial-grade motion reels from their actual garments, using presets instead of learning prompt workflows.

    Confidence · high

  12. 12

    Wholesale brand catalog refresh workflows

    You keep models and visuals consistent across the whole catalog so retailers see uniform product motion during seasonal refresh cycles.

    Confidence · high

— Principle

Honest is better than perfect.

RAWSHOT outputs include C2PA-signed provenance and clear AI labeling so your catalog operations can publish with confidence. In the EU AI Act context and California requirements, you get transparency artifacts you can pass through QA and review—without relying on vague “trust us” claims.

RAWSHOT · Editorial

Rights & provenance

Full commercial rights. Forever.

  • C2PA-signed on every image — EU AI Act Article 50 compliant
  • 28-attribute synthetic models — real-person likeness statistically impossible
  • Full commercial rights to every generation — no recurring licensing fees
  • Tokens never expire · One-click cancel · Transparent pricing

EU AI Act

C2PA

Commercial use

Pricing

~$0.22 per second of video.

~50–60 seconds per generation. Tokens never expire. Cancel in one click.

  • 01Video uses more tokens per second than stills — longer clips cost more.
  • 02The cancel button is on the pricing page.
  • 03No per-seat gates. No 'contact sales' walls for core features.
  • 04Failed generations refund their tokens.

FAQ

Practical answers on control, rights, pricing, scale, and compliant publishing.

Do I need to write prompts to use RAWSHOT?

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.

What does AI-assisted fashion video change for SKU-scale catalogs?

It turns catalog updates into a repeatable motion workflow, not a reshoot project. You click a scene direction, adjust framing and action with controls, and generate reels that stay garment-led.

RAWSHOT is built around the product, so cut, colour, pattern, logo, fabric behavior, and drape remain faithful. Combined with labeled provenance and an audit trail, your teams can publish faster while keeping QA and governance predictable.

Why skip reshooting every variant when you update colors or sizes?

Because reshoots force new days, new approvals, and new outcomes. When you update colors and sizes, you want visual consistency that matches your existing catalog style—without scheduling studio time for each change.

RAWSHOT helps by keeping model setup consistent across SKUs and letting you reuse the same direction while you swap inputs. You also get stable publish-ready outputs with full commercial rights and signed provenance metadata.

How do we turn flat garments into catalogue-ready motion without prompting?

You build the reel direction in the app: pick camera motion, framing, lighting, background, and model action. Every setting is a click or preset, so the garment remains the brief instead of being reshaped by language.

Once you generate, outputs include AI labeling and watermarking cues plus a signed audit trail per image. That makes it easier for teams to review, iterate, and ship the right look across channels.

Why does click-driven garment control beat prompt roulette for PDP reels?

Prompt roulette produces variance you can’t QA quickly—garment details shift, faces can change, and results can be hard to reproduce. With RAWSHOT, you steer with production-style controls so each iteration is about refining the look, not rewriting a text command.

That approach also keeps provenance and labeling consistent, with clear commercial rights for every output. The result is fewer surprises when your reel reaches production review.

What does RAWSHOT provide for rights and attribution on published reels?

Every output comes with full commercial rights, permanent and worldwide. You also receive provenance artifacts via C2PA-signed metadata and AI labeling, plus watermarking cues that support internal compliance and downstream transparency.

This removes the ambiguity that often slows publishing when teams receive outputs without a clear rights story or verifiable metadata. With an audit trail per image, review stays traceable for the whole catalog team.

How can we quality-check garment fidelity and consistency before publishing?

Use a simple QA loop: verify cut and placement details, confirm the garment’s color and pattern accuracy, and check that model framing stays consistent across variants. Then confirm the labeled provenance and watermark cues are present on the exported files.

RAWSHOT is engineered for garment fidelity and SKU consistency, so you’re reviewing predictable failure modes instead of chasing random output changes. That makes approvals faster for both campaign teams and catalog operators.

How do token costs work for video versus stills?

Video costs more because it uses more tokens per second than stills. For video reels, pricing is ~0.22 per second, and each generation typically takes about 50–60 seconds.

Tokens never expire, and failed generations refund their tokens. You also get a one-click cancel rule, so experimentation doesn’t turn into billing anxiety during catalog production.

Can we integrate reel generation into our existing catalog pipeline?

Yes. RAWSHOT supports both a browser GUI for single shoots and a REST API for catalog-scale pipelines, so your workflow can stay inside your production environment.

Instead of relying on prompt text as the control layer, you operate with the same garment-led controls across interfaces. That improves reproducibility for recurring launches, seasonal updates, and nightly batch runs.

If we’re scaling across teams, how do we avoid inconsistent results between operators?

Use the same scene-building controls and reuse your model setup across the catalog so operators aren’t “guessing” different creative directions. RAWSHOT’s consistency focus reduces drift between outputs, which is a common reason catalog teams struggle to keep pages coherent.

For collaboration, provenance and audit artifacts make review easier across roles—creative, QA, and publishing. When everyone generates with the same control system, throughput improves without sacrificing publish-ready standards.