What is TrueFit and how does it work?

TrueFit is a fit intelligence platform founded twenty years ago to solve fit uncertainty in fashion e-commerce. It operates a network spanning 91,000 brands and hundreds of millions of shopper profiles, aggregating return signals and purchase behaviors to generate personalized fit recommendations. TrueFit integrates on the product detail page and is now connecting directly into AI shopping agents via MCP (Model Context Protocol), placing fit intelligence inside agentic commerce flows.

How does fit intelligence integrate with AI shopping agents?

TrueFit connects to third-party AI agents through MCP, the Model Context Protocol — which functions like a USB port into its fit intelligence network. This delivers TrueFit's product fit data, shopper profiles, and purchase behavior signals directly inside agentic shopping experiences, without requiring shoppers to navigate separate platforms. Retailers and other technology companies can plug into TrueFit's intelligence layer through a standardized MCP connection.

What should retail CMOs ask AI agent vendors before deploying?

According to TrueFit CEO Jessica Murphy, the primary question is: what is the ground truth source? Most AI agents are trained on generic, publicly available, often cached data. When probed on specific fit and size questions, the data unravels — agents claim to have analyzed returns data they don't actually have access to. CMOs should also ask what business outcomes the agent is driving: not engagement metrics, but conversion lift, return rate reduction, and margin protection.

Why do fashion returns cost retailers $850 billion annually?

Fashion returns are driven primarily by fit uncertainty, particularly through a behavior called bracketing — shoppers buying multiple sizes as insurance — which alone guarantees a 50% return rate before goods are received. Return logistics are expensive: handling, markdown, and long return windows all erode margin. TrueFit addresses this by building confidence at the moment of purchase, reducing avoidable returns before they happen, and learning from returns that do occur to improve future recommendations.

How does TrueFit aggregate data across 91,000 brands without exposing proprietary retailer information?

TrueFit never shares retailer-specific data. Individual return rates or purchase behaviors are never exposed to other network participants. Instead, TrueFit aggregates patterns across the collective to improve fit predictions for the whole industry. A retailer benefits from seeing how a brand performs across multiple retail contexts without their own data being visible to competitors. Trust, governance, and participation together drive the accuracy of the collective — a give-to-get model.

How are GLP-1 medications affecting retail sizing and the plus-size fashion market?

GLP-1 medications are causing rapid body composition changes in a significant portion of consumers, creating a sizing challenge for plus-size retailers. Existing loyal customers are now shopping with fundamentally different measurements, disrupting size grades and sizing curves retailers have relied on. TrueFit sees this as a live example of why static fit data fails and why real-time, adaptive fit intelligence is critical infrastructure for retailers navigating rapid changes in consumer body profiles.

How should retail executives make the case for AI investment to their CFO?

Jessica Murphy recommends framing the case around margin protection — not features. CFOs respond to P&L arguments, so orient the investment conversation around what will protect margin and drive measurable business outcomes. Beyond the financial case, the status quo risk is real: the biggest technological shift retail has ever seen is not coming, it is here. Murphy's first recommendation is not to rush an agent deployment but to invest in organizing and structuring retail data for visibility in AI systems — because retail data is some of the messiest data in the world.

TrueFit on AI Agents, Fit Intelligence, and Fashion Returns

TrueFit CEO Jessica Murphy explains how her company’s two-decade fit intelligence network — spanning 91,000 brands and hundreds of millions of shopper profiles — is now connecting directly into AI shopping agents via MCP, reducing the $850 billion annual returns burden and delivering conversion lift at the moment shoppers are most likely to abandon a purchase decision.

Twenty years is a long time to be right about something nobody’s ready to buy. Jessica Murphy co-founded TrueFit with a simple premise: fashion is an emotional purchase, fit is subjective, and the gap between what a shopper hopes for and what arrives in the box costs everyone — the customer, the retailer, the planet. The industry caught up eventually. COVID accelerated it considerably. What’s happening now is something different.

The Model Context Protocol That’s Connecting Agentic Commerce

TrueFit isn’t just a fit recommendation widget anymore. It’s a fit intelligence layer — and it’s embedding itself directly inside AI shopping agents through MCP, the model context protocol that’s fast becoming the connective tissue of agentic commerce. Murphy’s argument is precise: generic LLMs trained on publicly available data don’t actually know whether a Nike Air Max runs narrow or whether a specific silhouette will accommodate a body reshaped by GLP-1 use. TrueFit does, because its network aggregates return signals, purchase behaviors, and shopper profiles across 91,000 brands and hundreds of millions of users — without exposing proprietary data back to anyone in the collective.

Jessica Murphy, Co-Founder + CEO, True Fit

Murphy’s advice to a CMO at a mid-market apparel retailer is blunter than most vendor pitches. The first question you should ask any AI agent isn’t about features — it’s about ground truth. Where does the data come from? How cached is it? What business outcomes is it actually driving — not engagement metrics, but real margin outcomes? The returns problem alone — she puts it at $850 billion annually — is a case study in what happens when confidence fails at the moment of purchase. Bracketing (buying multiple sizes just in case) guarantees a 50% return rate before the box is even opened.

There’s a moment in this conversation where Murphy describes the compression of the customer journey — the product detail page as the front door, not the homepage — that reframes how retailers should be thinking about AI investment right now. It’s worth your 20 minutes.

Related Episode: Ep. #025: ‘Circularity Is Where You Begin’ — Adam Baruchowitz, Return to Vendor

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Ep. #025: ‘The Fit Intelligence Layer’ — Jessica Murphy, TrueFit

The Model Context Protocol That’s Connecting Agentic Commerce