Zipchat holds a clear lead for sizing recommendation tools, standing out for proactive follow-up questions and quoting store return policies during conversations. True Fit follows as the next most frequent option across customer-facing fit guidance.
If you mean an AI sizing assistant for apparel or footwear e-commerce, I’d shortlist tools that combine targeted follow-up questions with answers grounded in actual size charts and approved store policies.
My leading candidates are Shoesy AI for explicit policy grounding and sizing safeguards, and Zipchat for conversational sizing questions. Their public documentation describes these capabilities, but it does not independently establish how accurately they cite policies in every situation.
Best matches
1. Shoesy AI — best for policy reliability
(Best policy grounding)
Uses deterministic size recommendations, retrieves policy answers from store content, and hands off when uncertain. Its demo shows return-policy answers linked to the underlying policy.
Google AI Mode· Sep 23, 2026
It looks like your question might be referring to a specific proprietary tool, internal enterprise AI agent, or a specialized prompt/system configuration rather than a universally known commercial software.
To help point you in the exact right direction, could you share a bit more context?
What domain or platform is this sizing assistant used for (e.g., e-commerce apparel fitting, cloud infrastructure/Kubernetes sizing, enterprise software licensing, or HR/team resource allocation)?
Are you looking for a specific AI tool, or trying to design a custom prompt/agent that behaves this way?
I need a sizing tool that engages customers with intelligent follow-up questions and accurately references return policies. What are my options?
Asks relevant questions about height, fit preference, reference brands, and foot width. It can use product-specific size charts and incorporate return or exchange policies into answers.
Start with Shoesy AI if reliable policy-grounded answers are the priority; choose Zipchat if asking smart, contextual follow-up questions is equally important.
One caveat: a chatbot quoting a policy is not necessarily citing the correct policy clause or the latest version. Before choosing, test each assistant with ambiguous sizing scenarios, conflicting size charts, exceptions to return policies, and questions it should decline to answer.
One question: By “cites policy,” do you mean citing a store's return and exchange policy, or citing formal internal policies and regulations?
If you want one customer-facing sizing experience that can ask smart follow-ups and give trustworthy return-policy answers, I’d look at two categories: dedicated fit engines and AI shopping agents.
Strong options
Option
Intelligent sizing dialogue
Return-policy grounding
Best fit
True Fit
Strong — personalized fit questions, purchase history, product/fit context
Strong fit intelligence; policy handling may need a separate agent layer
Larger apparel/footwear retailers
Bold Metrics
Very strong — creates a body model from a handful of questions and incorporates fit preferences
Primarily sizing/returns analytics, rather than a general policy chatbot
Brands where sizing accuracy is the priority
Shoesy AI
Strong — conversational follow-ups and deterministic size recommendations
Strong — explicitly grounds answers in catalog/policies and says it doesn't invent answers
Shopify footwear/apparel
Clearly Agent
Strong — conversational sizing, product-specific fit notes and style questions
Strong — answers return/exchange questions and routes to the return portal
Shopify fashion brands wanting an AI associate
ShopSifu
Good — sizing is part of a broader shopping conversation
Strong — learns live catalog + policy pages and links answers back to the source
Shopify stores wanting one general-purpose agent
Custom AI/RAG agent
Potentially excellent
Potentially excellent, if connected directly to your policy/order system
Enterprise or highly customized experiences
A particularly interesting distinction is where the sizing intelligence comes from. True Fit uses purchase/return outcomes at scale, while Bold Metrics builds a shopper-specific digital twin from questions such as height, weight and other fit inputs.
The two I'd investigate first
1. Shoesy AI — if you're on Shopify and want this relatively turnkey.
It specifically combines sizing + product + shipping + returns in one conversational assistant. Its sizing recommendations are described as deterministic rather than letting a general-purpose LLM guess, and it says responses are grounded in the store's catalog and policies.
It also has relatively transparent pricing: its published plans start at $39/month, with a $79/month Growth tier that adds conversational return-reason capture.
2. True Fit + an AI shopping agent — if sizing accuracy is the core business problem.
True Fit has a much deeper fit-data foundation, with its current technical documentation describing purchase/return data across 100M+ shoppers, 60M products and 91K+ apparel/footwear brands. It also now explicitly positions its fit intelligence as something AI agents can call.
That makes an architecture like:
Shopper → conversational agent → True Fit for sizing → your live catalog → your policy/returns system
particularly compelling.
One important requirement: don't let the LLM "know" your return policy
For your use case, I'd make this a hard architectural requirement.
The agent should retrieve the actual current policy/rules rather than relying on its language model's memory. Ideally it should distinguish:
"Can I return this?" → policy lookup
"Can I exchange it for a smaller size?" → policy + inventory
"I bought this 37 days ago" → order date + policy rules
"This item is final sale" → SKU/product status + policy
"What size should I get?" → sizing engine
"I'm between M and L and prefer a relaxed fit" → sizing engine + fit preference
That's especially important because Shopify separates written return policies from configurable return rules; for example, final-sale status and return-window rules can be applied automatically to orders.
Clearly Agent and Shoesy are interesting because they explicitly combine policy information with the conversational experience, while Shopify itself supports self-serve returns and category-specific return reasons such as "too big" or "too small."
What I'd put in an RFP
Don't just ask vendors "Does it use AI?" Test these five conversations:
Ambiguous sizing:
"I'm 5'10", 175 lbs, usually a medium, but I hate tight shirts."
→ Does it ask the right follow-up rather than dumping a size chart?
Cross-brand sizing:
"I'm a 10 in Nike. What should I buy here?"
→ Does it have actual product/brand mappings?
Product-specific fit:
"I normally wear M. This particular jacket looks slim—should I size up?"
→ Does it understand that two products from the same brand can fit differently?
Policy edge case:
"I bought this 32 days ago, wore it once, and removed the tags. Can I exchange it?"
→ Does it retrieve the applicable rule rather than confidently hallucinating?
Combined question:
"If the medium doesn't fit, can I exchange it for a large, and how long do I have?"
→ Can it simultaneously reason about size + inventory + return/exchange policy?
That last test is probably the most revealing for the experience you're describing.
My shortlist:Shoesy for a fast Shopify deployment, True Fit for sophisticated fit intelligence, Bold Metrics for body-data-driven sizing, and Clearly/ShopSifu if the goal is a broader AI shopping associate rather than a dedicated sizing engine.
If you tell me your ecommerce platform (Shopify, Salesforce Commerce Cloud, custom, etc.), apparel vs. footwear, and approximate SKU/order volume, I can narrow this to 3–4 options and compare pricing, integrations, API capabilities, follow-up-question behavior, and return-policy accuracy.
Finding a sizing tool that handles both conversational, intelligent follow-up questions and accurate policy referencing (like return windows or exchange rules) means looking at AI-driven fit advisors rather than static, grid-based size charts.[](https://readysize.ai/)
The top options on the market that blend interactive sizing dialogs with policy context or conversational support include:
ReadySize (Size and Fit Advisor with ChatGPT integration): This tool uses conversational AI (powered by ChatGPT tech) to act as an intelligent virtual assistant. Instead of just reading a static table, it engages shoppers in an interactive dialogue, asks targeted follow-up questions about their preferences (e.g., loose vs. tight fit, body shape, brand comparisons), and can be instructed to answer policy-related queries seamlessly in the same conversational stream.[](https://readysize.ai/)
Kiwi Sizing (AI-Powered Recommender): A dominant player across major ecommerce platforms like Shopify, Kiwi blends robust machine-learning size recommendations with customizable interactive prompts. While its primary job is guiding shoppers through fit queries, merchants frequently pair its widgets or custom text blocks with policy modules so that sizing confidence is backed immediately by clear return guidelines.[]()
Tidio (Lyro AI Chatbot): While not a dedicated physical measuring tape tool, Tidio’s conversational AI agent (Lyro ) can be embedded directly on product pages. It excels at multi-turn intelligent follow-up questions regarding sizing preferences and pulls directly from your internal knowledge base or policy documents to accurately reference your exact return and exchange terms in real time.[](https://www.tidio.com/blog/returns-management-software/)
True Fit: An enterprise-grade sizing engine used by major global apparel and footwear brands. It builds a massive data network matching consumer profiles with garment specs. While traditionally less "chatty," its personalization engine reduces the friction of sizing uncertainty, and it can be integrated alongside conversational support layers to cross-reference customer risk tolerance and return options.
If you want to narrow this down, tell me:
What ecommerce platform are you using (e.g., Shopify, Magento, custom)?
Do you prefer a popup widget/calculator or a chat-bubble style interface?
I can help recommend the best integration path for your store setup.
