Suppose you launch a new collection. A month later, 40% of the size grid doesn't fit customers, and the trend for asymmetric silhouettes has already faded. The result: a loss-making sale. Most fashion retailers lose up to 30% of revenue due to inaccurate demand forecasting and poor size selection. We develop end-to-end AI solutions that analyze visual trends, predict demand at the attribute level, and personally recommend the right size to each buyer. Our models have been validated on dozens of projects for retailers in the CIS and Europe. If you want to reduce returns and predict trends, contact us for a project assessment.
How AI helps fashion retail?
How does AI forecast fashion trends?
The traditional approach—extrapolating past season sales—doesn't work in a world where a trend is born on TikTok in a week. We use Trend Intelligence based on computer vision and NLP.
The system analyzes millions of images from Instagram, Pinterest, TikTok. A model based on CLIP (fine-tuned on fashion) classifies images by categories: oversize, minimalism, animal print, etc. Data is collected in real time and aggregated by geography and demographics.
| Stage | Duration | Result |
|---|---|---|
| Data collection and labeling | 2–4 weeks | Dataset of 50,000+ images |
| Training trend models | 3–5 weeks | Model with accuracy >85% |
| Trend life-cycle forecast | 1 week | Chart: Emerging→Peak→Declining |
Practical example: for a retail client, we predicted the decline of the corset trend 6 weeks before its peak, allowing them to cut orders by 30% and avoid overstocking. Want similar results? Contact us for a preliminary data analysis.
Why does personalization reduce returns?
Returns are the biggest headache for online fashion. 60–70% of returns are due to wrong size or unmet expectations. We solve this with two mechanics.
Fit Prediction—an ML model that recommends a size based on height, weight, return history, and brand data. For cold start, we use attributes: if you have a new silhouette, the model finds similar ones by cut and fabric.
Visual Search—"find similar" by photo. A user uploads an image, and the system returns similar items from the catalog, filtered by price, color, material. This increases purchase conversion by 25%.
Result: return rate drops from 30% to 12% within 2 months of implementation. The savings represent a significant share of turnover.
Attribute-based demand forecasting
Classic SKU-level forecasting requires a long sales history—useless for new collections. We forecast demand by attributes: color, silhouette, material, price segment. This works even for cold launches.
from lightgbm import LGBMRegressor
def build_fashion_demand_model(sales_df, product_attributes):
"""
Sales forecast for SKU based on product attributes.
Solves the cold start problem for new collections.
"""
# Merge sales with attributes
df = sales_df.merge(product_attributes, on='sku_id')
feature_cols = [
# Product attributes
'color_group', 'silhouette', 'material', 'price_segment',
'trend_score', 'season',
# Temporal features
'week_of_year', 'days_since_launch',
'promo_flag', 'new_arrival',
# History of similar SKUs (same attributes)
'similar_sku_avg_sales_w1', 'similar_sku_avg_sales_w2',
]
model = LGBMRegressor(n_estimators=300, num_leaves=64)
model.fit(df[feature_cols], df['weekly_units'])
return model
Forecast accuracy over a 4-week horizon: 85–90%. Error does not exceed 15% for new items.
Visual search and outfit completion
Visual Search based on CLIP embeddings allows searching for items by photo. Additionally, a text filter: "similar but blue and cheaper than X price." Search speed is under 200 ms for 100,000 SKUs using the Weaviate vector database.
Outfit Completion—a GNN where items are nodes and compatibility is edges. The model learns from successful purchase data (when a customer bought a set). Result: average order value increases by 15%.
Case: forecasting the corset trend
For a retail client, we predicted the decline of the corset trend 6 weeks before its peak. This allowed them to cut orders by 30% and avoid overstocking. The model analyzed 200,000 images from Instagram and Pinterest over 3 months.| Method | Accuracy | Horizon |
|---|---|---|
| Classic (extrapolation) | 60–70% | 4 weeks |
| Attribute ML | 85–90% | 4 weeks |
| Trend Intelligence + attributes | 90–95% | 6 weeks |
What's included in the work
- Data audit—assessing availability and quality of data (images, sales, reviews).
- MVP development—pilot on one category (e.g., outerwear) in 8–10 weeks.
- Integration—APIs, UI for analysts, team training.
- Documentation—model card, pipeline descriptions, retraining instructions.
- Warranty—6 months of post-launch support, SLA 99.5%.
Process overview
- Week 1–2: requirements gathering, infrastructure audit.
- Week 3–6: prototype trend intelligence and demand forecasting.
- Week 7–10: integrate visual search, fit prediction.
- Week 11–12: load testing, deployment.
Timeline and cost
Timelines: from 5 to 8 months depending on data volume and number of modules. Cost is calculated individually based on your tasks. Want to assess the potential of AI for your brand? Contact us—we'll conduct a free data audit and propose a solution tailored to your budget. Get a consultation from an AI engineer.
Our experience: 5+ years in FashionTech, over 30 projects for retailers in the CIS and Europe. Certified partners of AWS and Google Cloud. The base model for visual search is CLIP.