Vaidy Raghavan, Chief Product & Technology Officer, Xometry – Interview Series

Vaidy Raghavan, Chief Product & Technology Officer, Xometry, is an accomplished global technology executive and engineer who leads the company’s product and technology strategy, with a focus on scaling AI-driven marketplace capabilities that connect enterprise buyers with manufacturing suppliers. He brings deep expertise across AI, supply chain management, SaaS, and data analytics, having previously held senior leadership roles at companies like Wayfair, Microsoft, and Groupon, where he developed large-scale digital platforms and marketplace technologies. At Xometry, he is responsible for transforming complex manufacturing workflows into intelligent, data-driven systems that improve efficiency, resilience, and global supply chain connectivity.

Xometry is an AI-powered digital marketplace that enables businesses to source custom-manufactured parts on demand by connecting buyers with a global network of vetted suppliers across multiple production methods, including CNC machining, 3D printing, and injection molding. Founded in 2013 and headquartered in North Bethesda, Maryland, the company leverages machine learning to provide instant pricing, lead-time estimates, and supplier matching based on uploaded design files, streamlining the traditionally complex procurement process. With thousands of suppliers and tens of thousands of buyers worldwide, Xometry plays a central role in modernizing manufacturing by digitizing supply chains and enabling more agile, distributed production at scale.

You’ve had an incredible journey across Microsoft, Groupon, and Wayfair. What early experiences—personal or professional—shaped your interest in technology, and how did that eventually lead you to Xometry and the world of AI-powered manufacturing?

My interest in technology started early in my career. I’ve always been motivated by tough challenges and the chance to build solutions that actually move the needle in the real world.

In the fast-moving industries where I’ve spent my career, you have to strike the balance between moving quickly to bring an idea to life while also building durable and effective systems. Manufacturing exemplifies this well. It’s a deeply physical and deeply analog industry, but it also powers some of our most innovative systems.
Xometry sits at the intersection of all of that where we’re transforming a traditionally analog industry into something modern with real discipline and clarity on where we’re going next. For me, it’s a rare convergence of timing and purpose, and it’s exactly the kind of challenge I’ve been building toward for my entire career.

You’ve described manufacturing as the last “analog stronghold.” What are some of the biggest challenges AI is solving in manufacturing right now?

I describe manufacturing as the last “analog stronghold” because of its structural complexity given the manufacturing lifecycle is long and full of many handoffs. For example, during manufacturing, design and production engineering work alongside procurement, sourcing, quality, logistics, post-delivery assembly, and financial reconciliation across the supply chain, each stage introducing new risks and potential delays.

The core challenge is friction. At every point in the manufacturing chain, there are different formats, systems, and sometimes even units of measurement. Ideas pass through handoff after handoff, with each one becoming a potential failure point. Historically, the only way to manage that risk was manual human review.

AI is creating the most value right now in fighting that friction. It acts as a coordinator in that fragmented system: detecting discrepancies, matching parts to the right suppliers, and even dynamically modeling costs and lead times. It uses historical production data to predict where issues may arise, and flags them quickly before time and materials are wasted.

Suppliers get clearer intent and fewer surprises, meaning we can build trust with our network and help manufacturers produce the items we need.

In what ways has Xometry built trust with suppliers and buyers to adopt AI-driven workflows?

In manufacturing, trust is hard to earn given the stakes are high, the outcomes are irreversible and scrapped material, missed deadlines or quality failures can contribute to economic losses for a company. That’s why at Xometry, we earn trust by continually delivering reliability and clarity.

Suppliers and buyers rely on Xometry for speed and transparency. They know that when they upload a CAD file, our AI will quickly analyze the parts and generate estimates about pricing and potential risks. Predictions are grounded in real production data, which further builds that reliability and visibility. Pricing reflects real market conditions, and suppliers receive ongoing insights into how to improve performance and grow their business on the platform. The system also performs independent checks to catch discrepancies. When something doesn’t align, we surface it early and keep teams constantly informed.

How exactly does generative AI translate product ideas into buildable parts — and what impact does that have on development timelines?

Manufacturing has always struggled with the gap between intent and buildability. Early product ideas are often incomplete, and translating them into manufacturable designs requires multiple handoffs. That process is slow and often prone to reworks, which creates delays or shortages.

Generative AI compresses that loop. In practice, it translates partially structured inputs into manufacturable features. It can surface potential risks, suggest materials and processes, and flag constraints early. AI is reducing the friction that typically slows down production, slashing development timelines with fewer iterations and less scrap parts or materials.

How do you ensure quality and control remain high when processes become more autonomous?

One key principle is shifting quality checks to the earliest part of the production process. AI can analyze millions of geometric data points to help it determine feasibility of manufacturing, cost, and the best supplier match. This delivers precision and consistency without having to rely on human diligence alone, which has long been the only defense for risks during the quality control process.

That being said, keeping a human in the loop is still necessary for these augmented processes. We deploy AI to identify issues and alternatives when needed, but the final say for intervention lies with human operators who have the experience to make those decisions.

We see this especially in mission-critical sectors like aerospace and defense where having a human-in-the-loop is the only way to allow for automation at scale without sacrificing quality control.

How does AI-driven dynamic pricing work at Xometry, given variable manufacturing costs and supply chain complexities?

Manufacturing pricing is inherently variable because every part is different, and costs shift constantly based on materials, capacity, external factors like tariffs, and other constraints. Static pricing models don’t hold up in that environment.

At Xometry, dynamic pricing is a learning system. Our models are trained on millions of historical quotes and continuously updated with real production outcomes. That feedback loop keeps pricing grounded in reality.

When engineers upload a CAD file, our Instant Quoting Engine immediately analyzes the file and checks it against the external factors and constraints that impact pricing to identify the best manufacturer from our network of thousands of partners.