Fit Receipt

Product Introduction

1. Definition: Fit Receipt is a reference implementation of a virtual lingerie fitting room and AI-powered shopping assistant. Technically, it is a web application that combines a client-side virtual try-on interface with a server-side judgment execution model (JEV) for product recommendations.
2. Core Value Proposition: It exists to solve the high return rates and fit uncertainty in online lingerie shopping by providing transparent, AI-driven fitting advice. Its core value is delivering fast, calibrated confidence judgments on product suitability instead of generic essays, while prioritizing user privacy and data security.

Main Features

1. AI Judgment Receipt: Every product suggestion is accompanied by a detailed "receipt" explaining the AI's reasoning. This is powered by JEV (TypeSafe), a specialized judgment model that scores trade-offs (e.g., support vs. style, budget vs. material) and assigns a confidence score in approximately 300 milliseconds. Low-confidence results are flagged as unresolved, ensuring transparency.
2. Client-Side Photo Processing & Privacy-First Design: User photos for virtual try-on are processed entirely within the user's browser using Web APIs and are never uploaded to a server. The JEV model only receives typed, structured data fields (e.g., measurements, style preferences) and never has access to the user's photos, ensuring a high degree of privacy in virtual fitting.
3. Structured Agent & Fact-Guarded Workflow: The system uses an AI agent to guide a conversational chat interface. This agent's logic is constrained by underlying code that protects hard facts (like inventory) and pricing rules, preventing the AI from hallucinating or offering incorrect product details or discounts. This creates a reliable, fact-checked shopping assistant.

Problems Solved

1. Pain Point: High product return rates in e-commerce due to incorrect sizing, poor fit, and mismatched expectations, especially for complex apparel like bras and lingerie.
2. Target Audience: Direct-to-consumer (DTC) lingerie brands seeking to reduce operational costs from returns, e-commerce developers building trusted shopping experiences, and end consumers (primarily women) frustrated with the guesswork of buying intimate wear online.
3. Use Cases: A lingerie brand integrates Fit Receipt as its virtual fitting room to provide personalized style advice. A developer uses the open-source reference implementation to build a similar fact-guarded agent for eyewear or swimwear. A shopper uses it to confidently select a bra for a specific need (e.g., everyday comfort, special occasion) with clear reasoning provided.

Unique Advantages

1. Differentiation: Unlike standard virtual try-on tools that focus only on visual overlay or basic chatbots that give vague advice, Fit Receipt combines visual simulation with an explainable, confidence-scored AI judgment system. It also differs by not storing sensitive user imagery.
2. Key Innovation: The JEV (Judgment Execution Verifier) model architecture. It moves beyond generative text to produce structured, typed judgments with explicit confidence calibration. This allows the system to know when it doesn't know, improving trust and decision quality over black-box AI recommendations.

Frequently Asked Questions (FAQ)

1. How does Fit Receipt protect my privacy during virtual try-on? Fit Receipt uses a strict client-side processing model. Your photos are analyzed by your device's browser using HTML5 and JavaScript APIs and are never sent to or stored on any server. The AI that makes recommendations only sees the textual data you choose to provide.
2. What is an AI judgment receipt in online shopping? An AI judgment receipt is a detailed, structured breakdown of an AI's product recommendation. For Fit Receipt, it lists the factors considered (fit, support, budget), the trade-offs scored, and the final confidence level, making the AI's "thought process" transparent and auditable for the shopper.
3. Can Fit Receipt's technology be used for other types of clothing? Yes, as a reference implementation, its core architecture—the fact-guarded agent, the JEV judgment model, and the privacy-first client-side workflow—is designed to be adaptable. Developers can modify it for categories like swimwear, athletic wear, or formal attire where fit and personalization are critical.
4. How fast is the AI recommendation in Fit Receipt? The proprietary JEV model is optimized for speed, delivering a calibrated confidence judgment in approximately 300 milliseconds. This enables real-time, conversational recommendations without the lag typical of large language models generating long-form text.