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E-commerce / Logistics
EcoFashion Lider

Revolutionizing Used Clothing Valuation: Vision AI

A dedicated, custom-built Vision AI system that learns the specifics of your assortment. It cut per-item processing time by 41% and kept improving valuation accuracy week after week.

Vision AI
Python
React
TL;DR
Problem

The client (a clothing sorting facility) faced a surge in orders and a chronic staff shortage.

Solution

Deployment of a Vision AI system analyzing clothing photos in under 5 seconds. + Vision AI.

Result

Time saved: 42h / day

Time saved

42h / day
Growth beyond expectations

Efficiency gain

+41%

Return on investment

< 6 mo

Extra revenue

+120k PLN

The business challenge

The client (a clothing sorting facility) faced a surge in orders and a chronic staff shortage. Hiring new employees clashed with a high barrier to entry — accurate valuation required expert knowledge that took weeks to learn.

Methodology

How did we crack it?

Interactive Demo

See Vision AI in action

Our system analyzes hundreds of data points in a photo in a fraction of a second. See how the algorithm "sees" clothing.

Jacket analysis
Ready to scan

Analysis Results

ID: #8492-AX

Waiting for scan

In production, the system learns your specific brands and categories. The model reaches >90% accuracy after fine-tuning on your data.

vision_pipeline.py
1def analyze_item(image_tensor):# Custom pre-processing for warehouse lighting
2 features = backbone_resnet50(image_tensor)
3 brand_cls = brand_head(features) # Transfer Learning
4 defects = detect_defects_yolo(image_tensor)
5 valuation = price_estimator(brand_cls, condition, market_data)
6 if confidence < 0.85:
7 return flag_for_human_review(image_tensor) # Active Learning Loop
8 return generate_sku_metadata(valuation)
_
Under the Hood

A Dedicated AI Engine

This isn't an off-the-shelf plugin. We built our own image-processing pipeline in Python that integrates directly with the client's conveyor belt. The system has a feedback loop (Active Learning) — every correction made by an operator retrains the model, increasing its precision.

Process Optimization

Every second counts

We cut valuation time by 41%. At a scale of 5,000 items a day, that's 42 hours saved every single day.

Traditional Process

Duration60 sec / item
  • Manually searching for the model online
  • Subjective condition assessment (inconsistent)
  • Manual data entry into the system

Process with LinkWith.it

Duration35 sec / item
41% faster
  • AI recognizes brand and model in 3 seconds
  • Objective valuation based on market data
  • One click to approve

Scope of Work

The project took 4 weeks and covered the following stages:

  • Analysis of infrastructure and lighting
  • Implementation of the Vision AI model (GPT-4o / Claude 3.5)
  • Building the dedicated workflow
  • Building the operator interface (React)
  • Integration with the database and warehouse system
  • Staff training (shortened to 3 hours)
1

Analysis

Deployment of a Vision AI system analyzing clothing photos in under 5 seconds.

2

AI Implementation

Automatic identification of brand, type, condition, and defects.

3

Automation

A valuation algorithm based on real market data.

4

Scaling

A dedicated operator app with a 'One-Click-Verification' interface.

5

Scaling

An analytics dashboard for management showing category profitability.

Project Evolution

How do we work? (Iterative approach)

We don't guess. We delivered the project in proven phases, minimizing the client's risk at every step.

1

1. Discovery & Proof of Concept

3-4 weeks

Analysis of 300 clothing items, verification of lighting and hardware. A feasibility and ROI report.

2

2. Building the MVP System

8-10 weeks

Implementation of the Vision AI pipeline, database integration, and building the operator interface.

3

3. Scaling & Fine-tuning

6-12 months

Training a dedicated model on the client's data (>10k photos). Full integration with ERP and Allegro.

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