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Xometry Releases Comprehensive Upgrades to Its AI Model Architecture

(Positive)
Tags
AI

Xometry (NASDAQ: XMTR) announced comprehensive upgrades to its AI model architecture, deploying interconnected models across the manufacturing workflow that leverage proprietary data on geometry, manufacturability, pricing, supplier capability and production outcomes. The enhanced, context-aware process recommender now suggests optimal manufacturing processes from 20 techniques on first upload, with buyers accepting AI recommendations more than 85% of the time, according to Xometry.

New high-capacity cost models for CNC machining incorporate granular inputs such as geometry, material, finish and job composition, delivering an approximate 15% improvement in CNC cost-prediction accuracy in live testing. Adaptive sourcing models also dynamically price jobs and score supplier suitability using machine characteristics, quality history and on-time shipping records, which Xometry reports has significantly improved partner matching and created a more curated flow of jobs for supplier partners.

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Positive

  • AI process recommendations now accepted by buyers over 85% of the time
  • CNC cost-prediction accuracy improved by approximately 15% in live testing
  • 20 manufacturing techniques covered by upgraded AI process recommender
  • Adaptive sourcing models enhance supplier matching using capability and performance data

Negative

  • None.

News Market Reaction – XMTR

+0.08%
+0.08% News Effect

On the day this news was published, XMTR gained 0.08%, reflecting a mild positive market reaction.

Data tracked by StockTitan Argus on the day of publication.

Market Context

The four-event AI-tagged history averaged a 1.75% move. That platform record adds context to the arc...
Analysis

The four-event AI-tagged history averaged a 1.75% move. That platform record adds context to the architecture update, while recent net selling and low short positioning are relevant risks to monitor.

Key Figures

Recommendation acceptance: >85% CNC cost-prediction accuracy improvement: 15% Supported manufacturing techniques: 20 techniques +1 more
4 metrics
Recommendation acceptance >85% AI process recommender
CNC cost-prediction accuracy improvement 15% New cost-prediction models
Supported manufacturing techniques 20 techniques Buyer recommendations
Data dimensions 5 dimensions AI model architecture

Previous AI Reports

4 past events · Latest: Mar 03 (Positive)
Same Type Pattern 4 events
Date Event Sentiment 24h Move Catalyst
Mar 03 AI model expansion Positive -1.7% Expanded lead-time prediction and personalized pricing models inside the Instant Quoting Engine.
Feb 03 AI sourcing survey Positive +5.0% Survey reported aerospace leaders’ planned AI-enabled sourcing and domestic supply-chain investments.
Sep 08 Manufacturing outlook report Positive +4.3% Manufacturing Outlook identified AI, reshoring, quality, and pricing as competitiveness trends.
Nov 20 Supplier network expansion Positive -0.5% Marketplace surpassed 4,200 active suppliers and expanded multilingual quoting capabilities.

24h Move is the share-price change in the day after each event; other market factors may also have contributed.

Pattern Detected

AI-tagged announcements produced mixed reactions, with two aligned positive moves and two divergences.

Key Terms

cold-start, CNC machining
2 terms
cold-start technical
"solving the "cold-start" problem for new accounts"
A cold-start is the situation when a new product, platform, algorithm, or business has little or no historical data or existing users to learn from, making it hard to personalize services, forecast demand, or demonstrate traction. Think of it like opening a brand-new shop with no customers, reviews, or sales history: investors care because it increases uncertainty about how quickly adoption, revenue, or predictive models will ramp up.
CNC machining technical
"Starting with CNC machining, the new cost-prediction models"
Computer numerical control (CNC) machining is a manufacturing process where computer-programmed machines precisely cut, drill, or shape metal and plastic parts, acting like robot-guided tools in a workshop. Investors care because CNC capability affects a company’s cost, quality, production speed and ability to scale—similar to switching from hand tools to automated ovens in a bakery, it can lower unit costs, reduce errors and help meet higher demand reliably.

AI-generated analysis. How Rhea-AI works. Not financial advice.

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Leveraging Xometry’s proprietary data stack, the upgrades sharpen process recommendations, cost accuracy, and sourcing speed

  • Xometry’s upgraded context-aware AI process recommender now analyzes industry application and material choices alongside part geometry, solving the "cold-start" problem for new accounts and driving automated recommendation acceptance to more than 85%.
  • A new generation of high-capacity cost models, trained on Xometry’s proprietary data insights, delivers approximately 15% improvement in CNC cost-prediction accuracy.
  • The new adaptive sourcing models leverage Xometry’s proprietary data to create a more efficient experience for supplier partners, delivering significant improvements in partner-matching and sourcing speed.

NORTH BETHESDA, Md., July 20, 2026 (GLOBE NEWSWIRE) -- Xometry, Inc. (NASDAQ: XMTR), the global, AI-native marketplace connecting buyers and suppliers of custom manufacturing, today announced comprehensive upgrades to its AI model architecture, deploying a new generation of high-capacity, interconnected models that span the entire manufacturing journey. The upgrades leverage Xometry’s proprietary data insights across five critical dimensions: geometry, manufacturability, pricing, supplier capability, and production outcomes. The smarter the platform gets, the faster Xometry can turn complex engineering inputs into manufacturing decisions.

Smarter Process Recommendations From the First Upload

Xometry's upgraded context-aware AI process recommender understands a part's industry application – recognizing, for example, that an aerospace component likely calls for tighter tolerances and flight-grade alloys – along with its probable material and geometry, closing the gap for first-time customers who have no order history for the model to draw on. Buyers get a manufacturing recommendation when they upload a part, providing them with the optimal process from among 20 supported manufacturing techniques. Early feedback has been positive, with the AI recommendation being accepted by buyers more than 85% of the time – a marked improvement over the prior model, with the biggest gains among first-time customers.

A New Generation of Cost Models

The way Xometry prices custom jobs is evolving. Xometry's new generation of cost-prediction models prices each part based on the specific parameters required to manufacture it. The models consider a greater breadth and depth of inputs, including geometry, material, finish, and whether the part is a standalone part or one of several in a job. This granular input leads to more accurate pricing.

Starting with CNC machining, the new cost-prediction models are trained on Xometry’s proprietary data insights and understand the specific parameters each part requires, delivering an approximately 15% improvement in CNC cost-prediction accuracy, validated through live testing. The models predict a calibrated cost distribution for every CNC job, so Xometry can gauge and signal its confidence level before a quote is returned.

The company plans to extend the upgraded models to additional processes within Xometry’s Instant Quoting Engine® (IQE), and to incorporate additional CNC materials directly into IQE. As new materials are added, the models will continue to improve, built on a broader set of historical custom material requests.

Faster Sourcing, Better-Fitting Jobs for Supplier Partners

The new adaptive sourcing models leverage Xometry’s proprietary supplier data layer to price each job dynamically, moving quickly on well-understood jobs. The new models also incorporate an upgraded job-partner suitability that scores every job against a supplier partner's machine characteristics – including dimensions of the machine and sub-process capabilities – in addition to quality history and on-time shipping record. This has significantly improved partner-matching. Rather than navigating a noisy board of broad job notifications, partners are presented with a curated flow of better-fitting opportunities. This combination of deep capability matching and responsive pricing leads to a stronger network overall.

“Across the millions of parts quoted through Xometry, our AI is constantly learning. Our new models move beyond treating that knowledge as separate skills, where one model recommends how to build something, another prices it, another finds who should build it. Now, they are an intelligence layer that gets sharper every time a part moves through it. That’s what makes Xometry the digital infrastructure for custom manufacturing,” said Vaidy Raghavan, Chief Technology Officer at Xometry. “The data layers are what's actually deciding how a part gets made, what it costs, and who builds it. And the smarter that data gets, the more directly our customers feel it – more optimal recommendations, accurate pricing, better-fit suppliers – every time they upload a part.”

Learn more about Xometry.

About Xometry
Xometry’s (NASDAQ: XMTR) AI-native marketplace, popular Thomasnet® industrial sourcing platform and suite of cloud-based services are rapidly digitizing the manufacturing industry. Xometry provides manufacturers the critical resources they need to grow their businesses and streamlines the procurement process for buyers through real-time pricing and lead-time data. Learn more at xometry.com or follow Xometry on LinkedIn.

Media Contact
Lauran Cacciatori
VP Communications
773-610-0806
lauran.cacciatori@xometry.com

Investor Contact
Shawn Milne
VP Investor Relations
240-335-8132
shawn.milne@xometry.com


FAQ

What AI upgrades did Xometry (NASDAQ: XMTR) announce on July 20, 2026?

Xometry announced comprehensive upgrades to its AI model architecture, spanning process recommendations, cost prediction and sourcing. According to Xometry, the new interconnected models use proprietary data on geometry, manufacturability, pricing, supplier capability and production outcomes to support the entire custom manufacturing journey.

How much did Xometry improve CNC cost-prediction accuracy with its new AI models for XMTR?

Xometry reports an approximately 15% improvement in CNC cost-prediction accuracy with its new AI models. According to Xometry, the models use granular inputs like geometry, material, finish and job composition, and the improvement has been validated through live testing on CNC machining jobs.

What is Xometry’s new AI process recommender and how is it performing for XMTR customers?

Xometry’s upgraded AI process recommender suggests optimal manufacturing processes from 20 supported techniques on first part upload. According to Xometry, buyers accept these AI recommendations more than 85% of the time, with particularly strong gains among first-time customers lacking prior order history.

How do Xometry’s adaptive sourcing models affect supplier partners in the XMTR marketplace?

Xometry’s adaptive sourcing models dynamically price jobs and score job-partner suitability using machine characteristics, sub-process capabilities, quality history and on-time shipping. According to Xometry, this has significantly improved partner matching and gives suppliers a curated stream of better-fitting opportunities instead of broad, noisy job notifications.

Will Xometry extend its upgraded AI cost models beyond CNC machining for XMTR?

Xometry plans to extend the upgraded cost-prediction models beyond CNC machining to additional processes within its Instant Quoting Engine. According to Xometry, it also intends to incorporate more CNC materials, allowing the models to improve further as historical material request data expands.

How does Xometry use proprietary data in its new AI architecture for XMTR?

Xometry’s AI uses proprietary data across geometry, manufacturability, pricing, supplier capability and production outcomes to guide decisions. According to Xometry, these data layers determine how parts are made, what they cost and which suppliers build them, continually sharpening as more parts move through the platform.