Xometry
AI-optimized B2B marketplace (quoting and recommendations)
In the second quarter of 2026, Xometry improved its AI models' CNC cost prediction accuracy by about 15%, pushed process recommendation acceptance beyond 85%, and generated nearly 4,000 net new buyers through martech and personalization, on revenue of $229 million, up 41%.
Key points
- Xometry's proprietary AI models drive quoting, sourcing and process recommendations on the marketplace.
- CNC cost prediction accuracy improved by about 15%, with more than 85% of recommendations accepted.
- Martech and personalization: nearly 4,000 net new buyers in Q2 2026, the highest in 10 quarters.
- Level A evidence (Q2 2026 earnings call plus official release), living status confirmed.
Objective
Raise the conversion rate and value per buyer on the marketplace by making instant quotes and manufacturing recommendations more reliable, while lowering the cost of acquisition through martech and personalization.
The deployment
Xometry runs an on-demand manufacturing marketplace where a buyer uploads a part and receives an instant quote. In the second quarter of 2026, the company announced improvements to the proprietary AI models that handle costing, sourcing and process recommendations. The model reads each part's parameters and delivers about 15% more accuracy on CNC machining cost prediction, which strengthens buyer confidence and conversion. Buyers now accept the process recommendation more than 85% of the time, with the largest gains among new customers. In parallel, the marketing teams strengthened martech and personalization, which helped generate nearly 4,000 net new buyers over the quarter, the best level in ten quarters, while bringing marketplace advertising spend to a record low as a share of revenue. Revenue for the quarter reached $229 million, up 41%, with the marketplace growing 45% and active buyers reaching 89,557, up 20%.
Results Proof A
Results disclosed by Xometry on its Q2 2026 earnings call (verbatim transcript) and corroborated by its official quarterly results release. The figures on conversion, model accuracy and net new buyers are attributed by name to executives as part of financial reporting.
How it works
Inferred typical approachThe internal detail is not public. Here is a proven approach that leads to the same result, to adapt to your stack.
The stack in detail
- outil Moteur de devis IA Xometry (costing / sourcing) Proprietary AI models for cost prediction, sourcing and process recommendation, trained on a decade of quoting and part data. The sources do not name any third-party LLM.
- outil Couche martech et personnalisation Marketing stack that personalizes acquisition and the timing of sales outreach to raise net new buyers while lowering the share of advertising spend.
How it runs, concretely
For ops teams-
1Part upload client
The buyer uploads a file for the part to be manufactured to the marketplace.
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2Instant quote IA
The AI model reads the parameters, predicts the machining cost and proposes a price, with about 15% better accuracy on CNC.
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3Process recommendation IA
The model recommends the manufacturing process; the buyer accepts it more than 85% of the time, especially new customers.
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4Personalized acquisition marketing
Martech targets and personalizes acquisition and the timing of sales outreach to raise net new buyers at lower cost.
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5Model monitoring and upgrades equipe data
The data team tracks conversion and accuracy, and ships model improvements in cycles.
The parameters of the uploaded part and a decade of historical quoting data. Without this proprietary database, cost prediction and recommendations lose their accuracy.
How your customers perceive this type of use
Sourced studiesLe pricing algorithmique est le terrain le plus inflammable : 68% des consommateurs disent se sentir leses quand les marques utilisent le pricing dynamique et 80% jugent plus dignes de confiance les marques aux prix constants (Gartner, 2024). L'equite percue varie selon le secteur : le pricing dynamique n'est juge juste que par 33% a 40% des repondants selon qu'il s'agit de concerts ou de cinemas (YouGov, 17 marches). Le prix personnalise par les donnees individuelles est le plus rejete : 47% des Americains s'y opposent fermement (Consumer Reports, 2024).
Acceptance conditions
- La constance des prix comme signal de confiance : 80% jugent plus fiables les marques aux prix stables (Gartner 2024)
- Le secteur conditionne l'equite percue : le pricing dynamique est mieux tolere pour les cinemas (40% le jugent juste) que pour les concerts (33%) (YouGov 2024)
Red lines
- Le pricing dynamique percu comme abus : 68% se sentent leses (Gartner 2024)
- Le prix individualise a partir des donnees personnelles : 47% d'opposition ferme (Consumer Reports 2024)
- Les frais caches et hausses imprevues, vecus par 79% des consommateurs sur un an et associes a la perte de confiance (Gartner 2024)
Sources: Gartner 2024 · YouGov 2024 · Consumer Reports 2024
How to replicate
Inference, not sourcedData prerequisites
- A rich history of quotes and parts to train cost prediction
- Conversion data and recommendation acceptance data
- Behavioral buyer signals for acquisition targeting
Org prerequisites
- A data and product team able to maintain and retrain the models
- Alignment between data and marketing on the conversion and acquisition loop
- Quality governance for automated quotes
Possible stack
- In-house cost prediction models or models on cloud ML infrastructure
- A recommendation engine
- A martech layer for personalization and buyer scoring
The plan, step by step
- Step 1Consolidate the quoting and part history and measure current cost accuracy by manufacturing category.Deliverable: A baseline of accuracy and conversion by category.
- Step 2Train a cost prediction model on the highest-volume category and test it in shadow before production.Deliverable: A costing model validated on one category.
- Step 3Add a process recommendation model and measure the acceptance rate, especially among new buyers.Deliverable: Recommendations in production with acceptance tracking.
- Step 4Connect a martech personalization layer to target acquisition and sales timing.Deliverable: Personalized acquisition with cost-per-net-buyer tracking.
First step: Measure the current quote conversion rate and the recommendation acceptance rate, then focus first on making cost prediction reliable in the highest-volume category.
The same pattern, proven in other industries
Sources
- S1 Xometry (XMTR) Q2 2026 Earnings Call Transcript Primary archive pending
- S2 Xometry Reports Record Second Quarter 2026 Results Primary archive pending
- S3 Xometry Q2 2026 slides show 41% revenue growth, raised outlook Secondary archive pending
An error, newer info, a source?
This page lives on its accuracy. If a figure has moved, if the deployment has changed, or if you have a higher-quality source, tell us. Every sourced correction is verified before publication.