Garment exporter sample images and catalogs: halve the sampling communication cycle
Client Profile
Pain Points
Costly photo and retouch
Outsourced sample photography and retouching is slow and expensive.
Slow catalog production
Manual layout means each catalog revision takes days.
Fast style turnover
New styles launch faster than assets can keep up.
Live Demo
How We Built It
AI image retouching
Auto cutout, background swap and style unification with multi-background images.
Auto catalog layout
Auto layout catalogs from templates.
Bulk new-style updates
Bulk-generate assets for new styles.
Not betting on one AI — combining the right few
For a real business scenario, we don't lock in a single technical path up front. We evaluate candidate paths on the same benchmark, then design a hybrid so each path does what it's best at.
General LLM API
Public-cloud models such as GPT-4o / Qwen / DeepSeek
- ✓Out-of-the-box, minimal integration cost, cheap to start
- ✕Limited generalization to vertical domains, unstable field-level accuracy, separate compliance design
Industry fine-tuned model
Domain fine-tuning of open-source models on industry data
- ✓Grasps industry semantics, much higher accuracy, private deployment
- ✕Needs labeled data plus ongoing training and upkeep
RAG + rule engine
Retrieval-augmented knowledge plus business rule checks
- ✓Highly targeted, explainable, best latency and cost
- ✕Requires continuous data-loop investment
Fusion, not selection
What we deliver is not a single model but a fused pipeline of primary path + semantic correction + graceful fallback: routine cases are handled by the industry model in seconds; low-confidence fields are re-understood by an LLM; on service anomalies the system degrades to a general API and flags manual review — achieving accuracy, cost, latency and controllability at once.
An explainable, degradable, evolvable AI pipeline
We treat AI engineering as a pipeline, not a black box: every step has clear responsibilities, inputs/outputs and fallbacks. The five-step pipeline plus four-layer architecture below is the technical skeleton.
Ingestion & access layer
Multi-channel access with unified auth, idempotency and masking
AI inference layer
Multi-path inference plus a confidence router switching by features
Business orchestration layer
Connecting results to real business flows with rule checks and closure
Data loop layer
Every online error flows back; models iterate monthly
Tech Stack
Delivery Process
Asset standards
Defined visual standards for images and catalogs.
Image generation
Built AI retouching and multi-background generation.
Catalog system
Built auto catalog layout.
Trial
Small-batch trial.
Full use
Integrated into new-style launches.
Before vs After
“We used to wait on designers for catalog revisions. Now it's auto-generated and new styles reach clients fast.”
Where It Fits
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