--- title: "Building a Size and Fit Agent: AI-Powered Sizing Recommendations for Fashion Retail" description: "Learn how to build an AI agent that recommends accurate clothing sizes by mapping body measurements to brand-specific sizing charts, predicting fit preferences, and reducing return rates in fashion e-commerce." canonical: https://callsphere.ai/blog/building-size-fit-agent-ai-sizing-recommendations-fashion-retail category: "Learn Agentic AI" tags: ["Size Recommendation", "Fashion Tech", "Fit Prediction", "Retail AI", "Return Reduction"] author: "CallSphere Team" published: 2026-03-17T00:00:00.000Z updated: 2026-05-06T01:02:44.872Z --- # Building a Size and Fit Agent: AI-Powered Sizing Recommendations for Fashion Retail > Learn how to build an AI agent that recommends accurate clothing sizes by mapping body measurements to brand-specific sizing charts, predicting fit preferences, and reducing return rates in fashion e-commerce. ## The Sizing Problem in Online Fashion Size-related returns account for 30 to 40 percent of all fashion e-commerce returns. A "Medium" from one brand fits like a "Large" from another. Customers cannot try items on, so they either order multiple sizes or guess — both outcomes are expensive for retailers. An AI sizing agent solves this by mapping a customer's measurements and preferences to brand-specific sizing data. ## Modeling Size Charts The foundation is a structured representation of brand sizing data. Each brand-product combination maps size labels to measurement ranges in centimeters. ```mermaid flowchart LR REL(["Release of Building a Size and Fit Agent"]) NEW1["What's new flagship feature 1"] NEW2["What's new flagship feature 2"] NEW3["What's new flagship feature 3"] BREAK{"Breaking changes?"} MIG["Migration steps"] UPG(["Upgrade now"]) WAIT(["Pin current, upgrade later"]) REL --> NEW1 REL --> NEW2 REL --> NEW3 NEW1 --> BREAK NEW2 --> BREAK NEW3 --> BREAK BREAK -->|Yes| MIG --> UPG BREAK -->|No| UPG BREAK -->|Risk averse| WAIT style REL fill:#4f46e5,stroke:#4338ca,color:#fff style BREAK fill:#f59e0b,stroke:#d97706,color:#1f2937 style UPG fill:#059669,stroke:#047857,color:#fff style WAIT fill:#0ea5e9,stroke:#0369a1,color:#fff ``` ```python from agents import Agent, Runner, function_tool from typing import Optional # Brand sizing data: size -> measurement ranges in cm SIZE_CHARTS = { "BrandA_T-Shirt": { "S": {"chest": (86, 91), "waist": (71, 76), "length": 68}, "M": {"chest": (91, 97), "waist": (76, 81), "length": 71}, "L": {"chest": (97, 102), "waist": (81, 86), "length": 74}, "XL": {"chest": (102, 107), "waist": (86, 91), "length": 76}, }, "BrandB_T-Shirt": { "S": {"chest": (88, 94), "waist": (73, 78), "length": 70}, "M": {"chest": (94, 100), "waist": (78, 84), "length": 73}, "L": {"chest": (100, 106), "waist": (84, 90), "length": 76}, "XL": {"chest": (106, 112), "waist": (90, 96), "length": 79}, }, } FIT_PREFERENCES = { "slim": -2, # Subtract 2cm from measurements for tighter fit "regular": 0, "relaxed": 3, # Add 3cm for looser fit } @function_tool def recommend_size(brand_product: str, chest_cm: float, waist_cm: float, fit_preference: str = "regular") -> str: """Recommend a size based on body measurements and fit preference.""" chart = SIZE_CHARTS.get(brand_product) if not chart: return f"No sizing data available for {brand_product}." adjustment = FIT_PREFERENCES.get(fit_preference, 0) adjusted_chest = chest_cm - adjustment adjusted_waist = waist_cm - adjustment best_size = None best_score = float("inf") for size_label, measurements in chart.items(): chest_range = measurements["chest"] waist_range = measurements["waist"] chest_mid = (chest_range[0] + chest_range[1]) / 2 waist_mid = (waist_range[0] + waist_range[1]) / 2 score = abs(adjusted_chest - chest_mid) + abs(adjusted_waist - waist_mid) if score str: """Map a known size from one brand to the equivalent in another.""" source_chart = SIZE_CHARTS.get(source_brand) target_chart = SIZE_CHARTS.get(target_brand) if not source_chart or not target_chart: return "Sizing data not available for one or both brands." source_measurements = source_chart.get(source_size) if not source_measurements: return f"Size {source_size} not found for {source_brand}." # Find closest match in target brand source_chest_mid = sum(source_measurements["chest"]) / 2 source_waist_mid = sum(source_measurements["waist"]) / 2 best_size = None best_score = float("inf") for size_label, measurements in target_chart.items(): chest_mid = sum(measurements["chest"]) / 2 waist_mid = sum(measurements["waist"]) / 2 score = abs(source_chest_mid - chest_mid) + abs(source_waist_mid - waist_mid) if score str: """Get aggregated fit feedback from other customers.""" # In production, query your reviews database feedback = { "total_reviews": 234, "runs_small_pct": 15, "true_to_size_pct": 72, "runs_large_pct": 13, "common_note": "Sleeves run slightly long", } return ( f"Fit feedback for {product_id}: {feedback['true_to_size_pct']}% " f"say true to size, {feedback['runs_small_pct']}% runs small, " f"{feedback['runs_large_pct']}% runs large. " f"Note: {feedback['common_note']}" ) ``` ## Assembling the Size Agent ```python size_agent = Agent( name="Size and Fit Advisor", instructions="""You are a sizing expert for an online fashion store. Help customers find their perfect size. Process: 1. Ask for the customer's key measurements (chest, waist) in cm or inches 2. Ask about their fit preference (slim, regular, relaxed) 3. If they know their size in another brand, use cross-brand mapping 4. Check fit feedback from other customers for the specific item 5. Recommend a size with confidence level and explanation 6. Mention the return policy for size exchanges Always convert inches to cm internally (1 inch = 2.54 cm). If uncertain between two sizes, recommend the larger one and explain why.""", tools=[recommend_size, map_size_across_brands, get_fit_feedback_summary], ) result = Runner.run_sync( size_agent, "I wear a Medium in BrandA t-shirts. What size should I get in BrandB?", ) print(result.final_output) ``` ## Reducing Returns with Confidence Scores Add a confidence indicator that tells customers how reliable the recommendation is. High confidence means measurements fall squarely within a size range. Low confidence means the customer is between sizes and should consider their fit preference carefully. ```python def calculate_fit_confidence(chest_cm: float, waist_cm: float, size_range: dict) -> float: """Return 0-100 confidence score for a size recommendation.""" chest_low, chest_high = size_range["chest"] waist_low, waist_high = size_range["waist"] chest_in_range = chest_low <= chest_cm <= chest_high waist_in_range = waist_low <= waist_cm <= waist_high if chest_in_range and waist_in_range: return 95.0 elif chest_in_range or waist_in_range: return 70.0 else: return 45.0 ``` ## FAQ ### How do I handle customers who only know their measurements in inches? Build unit conversion directly into the agent's measurement collection flow. When a customer provides measurements, detect whether the values are likely inches (typically 30-50 for chest) or centimeters (typically 76-127 for chest). Confirm the unit with the customer and convert to your internal standard. Store both the original and converted values. ### How accurate are AI size recommendations compared to physical try-ons? With good sizing data and customer measurements, AI recommendations achieve 80 to 85 percent accuracy for basic garments like t-shirts and pants. Accuracy drops for items with complex fits like blazers or dresses. Incorporating community fit feedback ("runs small") and the customer's historical return data improves accuracy to 90 percent or higher over time. ### Should I store customer measurements for future visits? Yes, with explicit consent. Stored measurements allow instant recommendations on return visits without re-measuring. Implement this as an opt-in profile feature with clear data privacy disclosures. Let customers update measurements anytime and delete their data on request to comply with GDPR and CCPA requirements. --- #SizeRecommendation #FashionTech #FitPrediction #RetailAI #ReturnReduction #AgenticAI #LearnAI #AIEngineering --- Source: https://callsphere.ai/blog/building-size-fit-agent-ai-sizing-recommendations-fashion-retail