True Fit Announces Agentic Commerce Agent To Solve the Fit/Sizing-Driven $850B Returns Crisis Facing Fashion Online Retailers
Powered by over 20yrs of fit/size data across over $600b in transactional and return data from hundreds of millions of shoppers of 60m unique products and 91,000 apparel and footwear brands.
Today, True Fit is announcing two innovations. A new fit/sizing driven vertical agentic commerce agent for fashion and a MCP server for the True Fit data and consumer ‘size’ passport.
True Fit’s Moat: Proprietary Data
In the AI Era, competitive moats are being washed aways before our eyes. For example, outside our coverage focus area, in the last three weeks’ there’s a SaaSageddon/SaaSpocalypse going on in the public markets worried that the larger SaaS companies will die a death by a thousand vibe-coded cuts.
VCs widely agree that in this era, one of the remaining competitive moats is a proprietary data set. The perfect proprietary data set has these attributes:
Painstakingly gathered over years if not decades
Data network effects - a pool of data creates incentives for others to contribute and grows bigger like a snowball
Impossible to recreate with AI - Many previous data sets have fallen to AI companies creating pieces of it first with humans as a ‘kick start’ and then with increasingly less involved human data until the AI has closed the loop and is creating the data without human intervention.
Sounds nearly impossible to check all these boxes, but…
We Found One!
True Fit is in the enviable position of having a data-set that fits this pattern:
Painstakingly built over 20 years by gathered detailed fit feedback from hundreds of millions of shoppers using the True Fit system across 500 global retailers
Detailed SKU-level return data from that same pool of data
Then that data is mapped on top of the vast product catalog spanning over 90k brands and 60m products
Examples of True Fit Agentic Commerce Agent
Here’s a demo example of the True Fit Agent chatting with the customer and having a convo about the fit of an item→
Next, here we see four different use-cases where the True Fit Agent can add value to the consumer in an MCP type use-case where it’s ‘mixed in’ with a horizontal or vertical agentic shopping experience, or directly on a retailer/brands site.
Podcast Interview with True Fit CEO, Jessica Murphy
In this special breaking news episode of Retailgentic, I sat down with Jessica Murphy, Founder and CEO of True Fit.
In this episode, Jessica gives us the details of True Fit’s first specialized AI agent for size and fit, and tell us more about their amazing proprietary data set.
Listen/Watch Our Interview:
True Fit is evolving from a static “what size should I buy?” widget into a conversational consumer-oriented vertical shoppping agent that can handle the nuanced questions shoppers actually ask: comfort, flattering fit, fabric behavior, and edge cases like petite proportions. We also dig into how vertical agents (like True Fit) can accelerate horizontal super-agents via Model Context Protocol (MCP), and why the future of agentic shopping will be built on clean, structured, prioritized proprietary data, not scraped internet sentiment.
Highlights
True Fit’s specialized size + fit agent (and why it matters now)
Why 70% of fashion agent questions are about size/fit
The “Fit Passport”: from form-filling to natural conversation profiling
What generic agents miss: real-time sales + returns behavior, not just PDPs/reviews
Vertical vs. horizontal agents, and how True Fit uses MCP as an accelerant
The unglamorous aspect of building a competitive data moat: data cleaning, normalization, canonicalization
True Fit scale: 80M active users, hundreds of millions of profiles, ~100K brands, ~500 retailers
Agentic commerce is collapsing the Research/Find/Buy funnel, but fit is one of the biggest friction points left, then True Fit may have the unlock to the final piece of the puzzle.
Learn more here→
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Innovation solving a real challenge and ACTUAL friction in the consumer journey.