True Fit’s Jessica Murphy on the AI shopping agent built to solve fashion ecommerce’s biggest problem — fit
When True Fit unveiled its new AI shopping agent for fashion retailers on 17 February 2026, the Boston-based company positioned it as a direct response to online fashion’s most expensive moment of doubt: a shopper hovering over the size selector and thinking, “Will this fit?”
In an exclusive interview with MoveTheNeedle.news, co-founder and chief executive Jessica Murphy said the agent is designed to intervene in real time—on retailers’ product pages and in chat experiences—using outcomes from years of purchase and returns behaviour rather than generic web data.
The commercial pressure behind this category is significant. The National Retail Federation (NRF), in research produced with Happy Returns (a UPS company), projects $849.9bn in total retail returns in 2025, with 19.3% of online purchases expected to be returned.
In fashion e-commerce, fit and sizing uncertainty remains one of the largest drivers of those returns.
True Fit’s argument is that AI shopping assistants are only as useful as the data behind them—and that solving fashion’s sizing problem requires structured insights into what shoppers actually bought and kept.
Why sizing remains fashion ecommerce’s biggest barrier
Fit has long been the Achilles’ heel of online fashion retail. Consumers cannot try garments before purchasing, and sizing standards vary widely across brands, categories, and even individual products.
According to research cited by True Fit, fit and sizing uncertainty is the leading cause of purchase hesitation in fashion e-commerce and the most common question shoppers ask AI shopping assistants.
Murphy told MoveTheNeedle.news the challenge has remained consistent since the company’s founding.
“Retailers originally came to us for one reason: Help our shoppers figure out what size to buy. Trying to decipher size charts or guess at sizing was killing conversions and driving returns. The problem has not changed.”
What has changed, according to Murphy, is how quickly retailers must address that uncertainty.
“Shopping is becoming more conversational, and retailers need to resolve fit doubt in real time.”
What True Fit’s AI shopping agent actually does
True Fit describes its new system as a conversational AI shopping agent focused on fit and sizing guidance.
The agent can be embedded across digital retail channels, including:
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product detail pages
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product listing pages
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ecommerce chat experiences
It detects signals of hesitation during the shopping journey and responds with plain-language recommendations for size and fit, based on data from previous successful purchases.
Murphy argues the difference from many existing solutions is the data foundation.
“A lot of ‘fit tools’ are still basically static guidance. And a lot of AI assistants are working off public information, reviews, and whatever is on the product page. That is not enough to answer the question shoppers actually care about, which is ‘Will this fit me?’”
True Fit says its system analyses more than $616bn in retail transactions, covering hundreds of millions of shopper profiles, 60 million products, and more than 91,000 brands.
Why historical purchase data matters for AI shopping
The company’s central claim is that purchase outcomes—not opinions—are the most reliable indicator of fit.
Traditional tools often rely on size charts, reviews, or crowdsourced feedback. But these signals can be inconsistent or skewed toward a small number of vocal shoppers.
True Fit instead focuses on what consumers bought and kept, across time, across retailers, across products.
“This is not opinion data. And it’s not scraped content,” Murphy said.
Over time, this dataset allows the system to identify patterns between:
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shopper attributes
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garment characteristics
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successful purchases
Murphy said the company’s data network also benefits retailers beyond their own individual customer base.
“Retailers benefit from aggregated network insights that help reduce fit and sizing friction for shoppers, without exposing or sharing proprietary data.”
How the technology aims to reduce fashion ecommerce returns
Returns are one of the largest operational costs in ecommerce, particularly in fashion.
A key driver is “bracketing”—ordering the same product in multiple sizes with the intention of returning most of them.
Murphy said the AI shopping agent is designed to detect this behaviour and intervene before checkout.
“Bracketing is really a confidence problem. People order two or three sizes because they do not believe the site can accurately size them.”
The system can recognise signals such as a shopper adding the same item in multiple sizes and respond conversationally to guide them towards a single choice.
“You are not just managing returns downstream. You are preventing the fit mistake upstream.”
Where retailers will see business impact first
Murphy said retailers evaluating AI fit tools typically focus on three outcomes:
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conversion rate
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returns reduction
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customer lifetime value (CLV).
Conversion improvements usually appear first.
“Conversion is the fastest lever to see value, because fit doubt is the number one reason shoppers hesitate or abandon,” Murphy said.
Once shoppers consistently choose the correct size, return rates begin to decline.
Over time, retailers may also see improvements in customer lifetime value, as shoppers gain confidence in the retailer’s sizing guidance.
“A shopper who has a great first fit experience comes back more often and feels more confident spending more each time.”
Integrating fit intelligence into retail AI systems
The AI agent became available from 1 March 2026 for select early adopters, with a broader release planned for April 2026. True Fit says retailers can deploy the agent with minimal engineering effort, including on platforms such as Shopify.
The company also allows its Fit Intelligence to be delivered via Model Context Protocol (MCP), enabling other AI systems—such as search tools, shopping copilots, or personalisation engines—to use the same fit recommendations.
For retailers investing heavily in AI-driven ecommerce infrastructure, interoperability may be a critical requirement.
“The real unlock is whether the retailer is set up to take advantage of what the agent learns and surfaces once it’s running,” Murphy said.
How fit intelligence could influence product design
She also sees long-term implications beyond the shopping experience itself.
If retailers systematically capture fit outcomes, those insights could influence merchandising, inventory planning, and even garment design: “If fit intelligence becomes embedded in shopping agents, it stops being a front-end feature and starts becoming a feedback loop."
That feedback could help retailers identify which products consistently generate fit issues and adapt future collections accordingly.
Murphy describes this as a broader shift towards guided commerce grounded in real purchase outcomes.
“Once you solve fit, you can expand into guided shopping more broadly, including style and alternatives, in a way that still feels grounded in truth, not guesses.”
The next phase of AI shopping in fashion
The launch reflects a broader industry shift towards AI-powered shopping assistants and agentic commerce.
Many technology vendors are experimenting with AI agents that guide product discovery or automate purchasing tasks. True Fit’s approach focuses on a more specific—and persistent—problem.
Murphy believes fashion cannot simply rely on generic AI shopping tools.
“Fit is deeply personal, and preferences are unique to every shape and body. Solutions built on generic data don't help you answer the question everyone has: ‘Will this fit me?’"
For an industry where billions of dollars are lost each year to sizing mistakes, answering that one question remains one of e-commerce’s most valuable challenges.
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