Initializing ZYNG_OS v2.0
0%
ZYNG_OS v2.0 · Enterprise Visual AI

The Visual
Operating System
for Commerce

Ingest raw photography. Output commerce-ready assets across all channels - in milliseconds, at scale, with enterprise-grade auditability.

Explore Sandbox
ZYNG_OS · Visual Operating System
LIVE v2.0
Pillar result
Try-on result
Lock one selfie, map any garment — photoreal try-on in seconds.
01 Infinite combinations
Active
Pillar · Living Wardrobe Matrix

Infinite
Combinations

Mix and match garments to create multiple looks without reshoots. Enrich catalogs by creating combinations that work for differnet target groups.

Look 01 Casual Daywear Thread 1
01 Base Model
Female Base Model
02 Target Top
Female Top 1
03 Target Bottom
Female Bottom 1
04 Rendered Output
Female Output 1
Look 02 Casual Daywear Thread 2
01 Base Model
Male Base Model
02 Target Top
Male Top 1
03 Target Bottom
Male Bottom 1
04 Rendered Output
Male Output 1
Look 03 Sporty Comfort Thread 3
01 Base Model
Female Base Model
02 Target Top
Female Top 2
03 Target Bottom
Female Bottom 2
04 Rendered Output
Female Output 2
Look 04 Sporty Comfort Thread 4
01 Base Model
Male Base Model
02 Target Top
Male Top 2
03 Target Bottom
Male Bottom 2
04 Rendered Output
Male Output 2
Look 05 Evening Wear Thread 5
01 Base Model
Female Base Model
02 Target Top
Female Top 3
03 Target Bottom
Female Bottom 3
04 Rendered Output
Female Output 3
Look 06 Evening Wear Thread 6
01 Base Model
Male Base Model
02 Target Top
Male Top 3
03 Target Bottom
Male Bottom 3
04 Rendered Output
Male Output 3
Look 07 Modern Street Thread 7
01 Base Model
Female Base Model
02 Target Top
Female Top 4
03 Target Bottom
Female Bottom 4
04 Rendered Output
Female Output 4
Look 08 Modern Street Thread 8
01 Base Model
Male Base Model
02 Target Top
Male Top 4
03 Target Bottom
Male Bottom 4
04 Rendered Output
Male Output 4
Deploy Wardrobe Engine
Generate look matrices at scale without reshoots.
Know More →
02 Virtual Try-On
Scanning
Pillar · Interactive Mirror

Real-Time Virtual Try-On

Instant visualization mapping fabric drape and geometry. By matching a single base user selfie against target garments, our custom-trained diffusion models align folds, shadows, and posture in under 2 seconds.

<2s Latency
0.3x Inference Cost
Custom In-house model
TARGET IDENTITY LOCKED
Source User Selfie
ID_08239_MATCHED
FaceID Mapping: OK Keypoints: 128/128
GENERATION STREAM BATCH OUTPUTS
Generative Look 1
BATCH_01 1.1s
Generative Look 2
BATCH_02 1.2s
DASHBOARD PIPELINE STEPS
Look 01 · Streetwear Base
1.1s
Selfie Input
User Selfie Target
Target Garment
Streetwear Garment Reference
SYNTHESIZE
VTON Latent Output
Streetwear Try-On Output
Aligns oversized cotton hoodie drapes and shoulder seam placements to user proportions while preserving texture and base lighting parameters.
Look 02 · Layered Outerwear
1.2s
Selfie Input
User Selfie Target
Target Garment
Jacket Garment Reference
SYNTHESIZE
VTON Latent Output
Jacket Try-On Output
Calculates fold geometry for heavy leather outerwear overlays, mapping highlights cleanly along the shoulder angles.
Look 03 · Smart Casual Oxford
1.0s
Selfie Input
User Selfie Target
Target Garment
Smart Casual Reference
SYNTHESIZE
VTON Latent Output
Smart Casual Try-On Output
Maintains rigid collar alignment and button placket structure, adapting smoothly to base posture variations.
Look 04 · Tailored Structure
1.1s
Selfie Input
User Selfie Target
Target Garment
Tailored Blazer Reference
SYNTHESIZE
VTON Latent Output
Tailored Blazer Try-On Output
Sews complex layered blazer configurations with soft under-layers while conserving base skin tones and wrist junctions.
Deploy Try-On Engine
Outfit visualization rendered in under two seconds.
Know More →
03 Vision QC
Ready
Pillar · Defensive Radar

Vision QC
at Ingestion

Stopping bad assets before they reach your commerce stack. Automated IP detection, blur analysis, hallucinated text flagging, incorrect cropping and custom rules.

IMG_4471
IMG_4471.raw
✓ Human: Detected
✓ Mens Shirts
✓ Shot: Full Body
IMG_4472
IMG_4472.raw
✓ Mens Bottoms
✓ Crop: Valid
✓ Blur: Pass
IMG_4473
IMG_4473.raw
✓ Human: Detected
✓ Mens T-Shirt
✓ Shot: Full Body
IMG_4475
IMG_4475.raw
✗ Blur: Pass
✗ Human: Not detected
✗ Category: Mens Topwear
NO HUMAN DETECTED
·Reject
IMG_4475
IMG_4475.raw
✗ Blur: fail
✗ Crop: incorrect
✗ Sharpness: 0.24
BLUR + CROP FAIL
Sharpness 0.24 · Reject
IMG_4476
IMG_4476.raw
✗ Unauthorized IP
✗ Logo detected
✗ Conf: 97.4%
IP INFRINGEMENT
Conf: 97.4% · Image Flagged
↓
Flagged Images Routing Active
2 assets flagged
2
Rejected
4
Approved
vision-qc-engine://pipeline-analysis
Processed
Raw input
Raw Image
Result
Result Image
Background removal
BG Removed
Target Bounding Box
Identify Image
[OK] Model pipeline run completed for target model image.
Category:"Sweatshirts"
Gender:"Women"
Process:Yes
Next Step:"complete"
Count:1
Blur Flag:N/A
Gibberish Flag:N/A
Simple Edit Flag:Yes
Alignment:Yes
Extension:No
Human Model:Yes
Real Image:Yes
Angle Type:"front"
No. Side Touch:0
Product Missing:No
Proper Size:Yes
Subject Clear:Yes
Subject Det.:Yes
Cosine Value:1.000
Deploy QC Vision Engine
Automated brand compliance across every catalog image.
Know More →
04 Smart standardisation
Live
Pillar · Kinematic Grid

Smart
Standardization

Body and margin aware cropping for omni-channel resizing. Content and context aware cropping/generation supporting all aspect ratios and image formats.

Output Spec Auto applied
1:1
Width1080px
Height1080px
Align XCenter
Align YCenter
Margins
T6 B6 L8 R8 Unit%
Format
PNGJPGJPEGWEBPAVIFTIFF
Quality
100%
DPI72 SizeOptimized
Input · raw asset
Output · optimized channels
Image 01
Raw input image 1
1:1 crop
1:1
Instagram
feed
2:3 crop
2:3
Portrait
print
9:16 crop
9:16
Stories
/ Reels
Image 02
Raw input image 2
1:1 crop
1:1
Instagram
feed
2:3 crop
2:3
Portrait
print
9:16 crop
9:16
Stories
/ Reels
Image 03
Raw input image 3
1:1 crop
1:1
Instagram
feed
2:3 crop
2:3
Portrait
print
9:16 crop
9:16
Stories
/ Reels
Deploy Image Engine
Commerce-ready assets from raw photography, instantly.
Use API → Try Sandbox → Know More →
5 Visual Discovery
Active
Feature · Visual Discovery

Visual
Similarity.

Vector-based clothing retrieval. Upload any reference image, and the AI instantly maps style, fabric, and cut against your entire catalog.

API · Coming Soon
Visual Similarity Matrix
Feature · Image-to-Product

Visual
Search.

Snap a photo. Find the exact match. Empower users to search your entire catalog using their smartphone camera or uploaded inspiration images.

API · Coming Soon
Visual Search Interface Schematic
06 Studio Toolkit
Modular
Pillar 05 · The Studio Toolkit

Granular control.
Infinite possibilities.

Beyond automated pipelines, ZYNG OS provides direct API access to individual visual intelligence endpoints along with a sandbox to build bespoke workflows across bulk images for your exact studio needs.

Model Swap

Model Swap

Change base models instantly while retaining the exact physical fit, fabric drape, and studio lighting of the original source garment.

Subject Focus

Subject Focus

AI instantly isolates the main subject, automatically removing background clutter or replacing it entirely with studio-perfect environments.

AI Colour Shift

AI Colour Shift

Change background colors dynamically based on SKU data. Shoot one base colorway, let AI accurately render the entire seasonal palette.

Smart Swatch

Smart Swatch

Extract hyper-accurate color profiles and physical texture swatches automatically from source garments to enrich PDP metadata instantly.

Magic Resize

Magic Resize

Intelligently upscale, outpaint, and reframe assets for any aspect ratio without losing subject integrity or introducing artifacting.

Precision Crop

Precision Crop

Automatically detect and isolate specific body parts (e.g., shoes, bags, collars) to generate focused, high-detail macro shots at scale.

Test Vision Intelligence End Points

Upload your asset and see real-time transformation.

Try in Sandbox → See More Use Cases →
07 Workflow Engine
Composing
Workflow Engine

One pipeline.
Rules per image.

Chain any steps into a single pipeline, then branch on what each image contains. At ingestion the engine reads every upload and routes it down the matching path — applying the right crop, margins and QC for that input type, with no manual sorting.

Customisableany step, any order Chainedoutput feeds the next Conditionalrules branch on content
Conditional routing

Same pipeline, two kinds of input. Each branch applies its own dimensions, margins and quality gates — two examples below.

Full human
5% margin 1080 × 1440

Full-length model shots are framed tight with a 5% margin and standardised to a 3:4 PDP crop.

Raw full-length model input Input
→
Standardised 1080 x 1440 output Output
QC checks
  • ✓Head and feet fully in frame
  • ✓5% safe margin on all sides
  • ✓Face sharp · no crop through joints
Product only
10% margin 1000 × 1500

Still-life and pack shots get more breathing room — a 10% margin and a taller 2:3 frame.

Raw product-only input Input
→
Standardised 1000 x 1500 output Output
QC checks
  • ✓10% margin even on all sides
  • ✓Background ≥ 95% clean & uniform
  • ✓Product centred · no shadow clip
08 Case Studies
Verified
Enterprise Verification

Real-World Case Studies

How teams use ZYNG's auto-formatting core in production — the problem they faced, what we deployed, and the measured result.

View All Case Studies
Customer profile · Image Standardisation

India's Largest Fashion Ecommerce Platform

High-volume fashion catalogue · national scale
450K+
Images processed in a day
80,000+ SKUs standardised in a single production run.
The challenge

One of India's largest fashion ecommerce platforms needed to standardise an enormous catalogue at speed — consistent imagery across tens of thousands of SKUs was critical to their listing quality.

The solution

ZYNG's standardisation API processed the full catalogue via image links — no manual intervention, no file transfers. Over 450,000 images across 80,000+ SKUs were standardised within a single day.

What was achieved
80K+
SKUs standardised
1 day
Full catalogue processed
$source · image link ingestion · active
$standardisation pipeline · running
$catalogue output · delivered
CATALOGUE · STANDARDISED LIVE LINK
Customer profile · Vision QC at Scale

Global DAM Provider

Multi-tenant asset management platform
Millions
Images QC'd via checkpoints
Platform-wide quality enforcement without manual review.
The challenge

A global DAM provider needed to help their platform customers QC millions of images consistently — manual checks weren't scalable and non-compliant assets were slipping through.

The solution

ZYNG's QC checkpoints were embedded directly into the DAM's ingestion layer, automatically scanning every asset against configurable brand and quality rules at platform scale.

What was achieved
Platform
QC enforced across all tenants
Zero
Manual reviews needed
$checkpoint scan · running
$brand rules applied · pass
$asset approved · routed to catalogue
QC PIPELINE · ACTIVE LIVE LINK
Customer profile · Catalogue Enrichment

Australian Retail Brand

Omnichannel apparel retailer · regional catalogue
Thousands
Fresh looks generated
New catalogue combinations without a single reshoot.
The challenge

The brand wanted to present more looks across their catalogue but couldn't justify the time and cost of additional studio shoots for every combination.

The solution

Using ZYNG's mix-and-match function, the brand generated thousands of fresh outfit combinations from their existing catalogue assets — enriching their product pages with new looks instantly.

What was achieved
1000s
New look combinations created
Zero
Additional studio shoots needed
$mix-and-match · combinations generated
$catalogue enriched · new looks ready
$product pages updated · live
CATALOGUE · ENRICHED LIVE LINK
API Gateway · All Systems Nominal

API-First Visual
Infrastructure

Developer-centric architecture designed for programmatic commerce workflows. By training in-house model systems, we deliver automated visual pipelines with complete, robust data security.

GDPR (In Progress) SOC 2 Type II (In Progress) ISO 27001 (In Progress) AES-256 Encrypted
09 Comparison
Benchmark
How We Compare

One stack.
Not four.

ZYNG replaces the patchwork. Here's what teams switch from.

Replaces the patchwork
PhotoshopPoint tools ×4Legacy DAMManual QC
Capability Our Platform ZYNG OS Photoshop Point Tools ×4 Legacy DAM
Smart crop & resize
Body-aware, all ratios 		✓ Manual Partial ✕
Generative padding
AI background extension 		✓ Beta ✕ ✕
Vision QC at ingestion
Blur, IP, feedback scan 		✓ ✕ Partial ✕
Wardrobe matrix
FaceID-locked outfit swap 		✓ ✕ ✕ ✕
Flagged Images routing
Webhook · auto re-shoot 		✓ ✕ Manual Partial
SOC 2 / GDPR ready
Enterprise compliance 		Under process ✓ Varies Partial
API-first integration
✓ ✕ Partial Partial
Built In-House

Our own models.
Our own pipelines.

Every ZYNG capability runs on models and pipelines we build and train ourselves — engineered for flexibility across whatever you ingest and whatever you need to ship. Our expertise runs deepest where it counts: vision models purpose-built for e-commerce and fashion, at scale.

Custom-trained models Flexible across input & output Fashion & commerce vision specialists
Book a Demo → See More Use Cases → 15-min walkthrough on your own catalog
10 Pricing
Live
Pricing · Pay-per-credit

Simple, transparent pricing

No subscriptions — you’re billed only for the credits you use (1 credit = 1 output image). The more you process, the lower your rate.

Get 100 free credits when you sign up
Estimate your volume 2,000 images
10050020K100K100K+
HD

HD Plan

High-quality edits for standard resolutions

$0.031/ image
at 2,000 images
0–500$0.063Base
500–20K$0.031−51%
20K–100K$0.016−75%
100K+CustomVolume
Est. — total
Get Started with HD →
4K

4K Plan

High-quality edits for premium resolutions

$0.063/ image
at 2,000 images
0–500$0.125Base
500–20K$0.063−50%
20K–100K$0.031−75%
100K+CustomVolume
Est. — total
Get Started with 4K →
8K

8K Plan

Ultra-high quality for professional content

$0.156/ image
at 2,000 images
0–500$0.313Base
500–20K$0.156−50%
20K–100K$0.078−75%
100K+CustomVolume
Est. — total
Get Started with 8K →
How credit consumption works
⚙ 1 Workflow Credit Multiple automated transformation or editing steps are allowed, limited to one final output image asset per credit.
✓ 1 Quality Control Credit One complete, automated visual intelligence diagnostics and standards scan at ingestion.
★ Enterprise Tiers Substantial discounts on high-volume credit commitments. Custom deployments and dedicated pipelines available.
11 FAQ
Open
FAQ

Common
questions.

Still deciding? These are the detailed questions our engineering and catalog teams answer most.

Bulk image ingestion is handled via image links — simply provide the URLs and the platform processes them at scale. There is no freshness requirement, and the system handles both legacy back-catalogs and ongoing production workflows equally well.
Our pricing is consumption-based and scales with your production volumes. Workflow credits cover image transformation and output, while QC credits cover automated quality scanning at ingestion. High-volume enterprise customers can secure custom annual agreements with volume-based discounts.
Your assets are stored in encrypted storage . Our SOC 2 and GDPR certifications are currently underway.
ZYNG OS is built as a modular system — each capability can be used independently or as part of a full pipeline. Depending on the module and volume requirements, we work with your team to define the right API specifications and integration approach.
All generation and editing tasks run on our proprietary, in-house AI models, so you never rely on third-party APIs. Your brand assets and photography are completely isolated from other customers. We do not train models on client data without explicit enterprise agreements, keeping your brand IP safe and secure.
Yes. You can define custom brand rules covering margins, background tolerances, resolution limits, and watermarking thresholds. Enterprise and large-volume customers can explore further customisation beyond the standard rule set depending on their specific requirements.
Get in touch

Write to us at

contact@zyngai.com