Xometry introduced its Enterprise Machining Lead Time Prediction Model, trained on a dataset four times larger than its predecessor and powered by deep learning on global supplier performance data. The model delivers superior RMSLE accuracy, expands one-day lead-time options across an increasing catalog of materials and geometries, and reduces standard lead times by optimizing operational throughput.

Xometry enhanced its dynamic pricing logic within the Instant Quoting Engine by deploying conversion-rate models that analyze part geometry, quote configurations, and customer-specific history to generate individualized price-response functions. Following successful Q4 2025 testing, this pricing model begins broad rollout to U.S. customers in Q1 2026 with the goal of improving buyer and supplier experiences and increasing revenue per user.

By embedding both predictive lead-time intelligence and dynamic pricing directly into live marketplace transactions, Xometry creates a closed-loop system where digital quoting, supplier selection, production performance, and delivery outcomes continuously inform model retraining. This integration accelerates time to market, improves delivery reliability, and reinforces Xometry’s competitive advantage in AI-powered industrial sourcing.