How Smart Glasses Are Built

Introduction

With the rapid progress of AI, optics, and miniaturized electronics, more and more companies are exploring the possibility of creating “next-generation personal devices” that sit directly on the face. But between concept and reality lies a long and complex path.

So what does it really take to develop a smart glasses product? This article breaks it down into three core capabilities:

• Software & System Design (the brain)
• Industrial Design & Integration (the skeleton)
• Hardware Production & Supply Chain (the muscle)

Each plays a critical role—and without any one of them, the product is unlikely to succeed.

1. Brain: Software & System Design

In the development of smart glasses, software and system design serve as the foundation for the overall functional logic of the device. It determines what tasks the device can perform, how it interacts with users, and how data is processed and synchronized. Compared to hardware production and industrial design, system-level challenges are often "invisible yet critical."

From an architectural perspective, the system design of smart glasses mainly includes the following core modules:

• Operating System Layer: Determines whether the system uses a self-developed kernel or is built on Android, Linux, or RTOS.
• Interaction Logic Design: Covers methods such as voice activation, gesture control, eye-tracking, head movement response, and AR pop-ups.
• AI Capability Integration: Includes local/cloud-based deployment of models for voice recognition, real-time translation, image recognition, and personalized assistants.
• Data Processing & Synchronization Mechanisms: Involves local caching, cloud connectivity, permission management, and privacy control.
• Development Tools & Ecosystem Support: Includes whether an SDK is provided, whether third-party app integration is allowed, and whether multi-platform deployment is supported.

System design not only defines the functional boundaries of the product but also directly impacts developer engagement and ecosystem development efficiency. A flexible, open, and easily integrable system architecture can significantly lower the barrier to implementing new features and accelerate product iteration.

Currently, smart glasses products on the market show considerable variation in their system-level choices. Different brands make trade-offs based on their positioning, hardware capabilities, and target markets. The industry has yet to converge on a unified mainstream platform, with most systems still in the early exploratory stages or customized for specific scenarios. Generalizability and scalability remain areas in need of improvement.

Against this backdrop, San Francisco-based startup Mentra launched its open-source operating system MentraOS 2.0, aiming to fill the system-level gap in the XR space. Mentra’s mission is clear: to build a foundational software platform for the next generation of lightweight XR (Extended Reality) glasses. Recently, the company announced an $8 million funding round, giving a strong boost to the official launch of MentraOS. Founder and CEO Cayden Pierce described the platform as the "Android for smart glasses," designed to support all-day wear, real-time AI, and cloud-native applications.

This vision is both ambitious and highly relevant. Pierce stated: “The hardware is finally ready, the AI is here—but there’s still no OS,” Pierce said. “We’re not building hype demos. We’re building the infrastructure for the next personal computer.”

Unlike the vertically integrated and closed ecosystems being built by XR giants like Meta and Google, Mentra is taking a different path—openness, modularity, and cross-device compatibility. With an open SDK, developers can build apps compatible with multiple hardware platforms. The Mentra App Store supports multitasking, allowing applications to run in parallel and access real-time sensor data and user context. This design philosophy aligns with the expectation that smart glasses will be worn all day.

While the industry has yet to settle on a dominant platform, emerging systems like MentraOS are laying a more flexible and ecosystem-friendly software foundation for smart glasses. This trend opens new opportunities for downstream manufacturers and developers to explore.

2. Skeleton: Industrial Design & Integration

Once the hardware and software foundations are in place, industrial design and module integration become the key bridge to “bring functionality to life.” Industrial design is not only an expression of product aesthetics—it serves as the physical skeleton that enables functionality, houses the system, and enhances the wearing experience. Like the bones in the human body, it determines the structure, support, and coordination of the entire device.

From concept to mass production, what core challenges must the “skeleton” of a smart glasses product solve?

The first major challenge is spatial layout. 

Compared to smartphones or headsets, smart glasses have extremely limited internal space, yet must house nearly the same complex modules—main processor, battery, display, microphones, speakers, cameras, sensors, buttons, and more. Within this millimeter-scale design, industrial designers must address several structural issues: how to arrange modules efficiently, manage heat zones, and balance function and weight between the left and right temple arms.

We can see a good example in the smart glasses co-developed by Ray-Ban and Meta. They positioned camera modules on both sides of the frame and separated the processor and battery into each temple arm, maintaining symmetrical weight distribution. This approach conceals the tech and makes the glasses look almost identical to traditional sunglasses.

The second challenge is wearing comfort. 

Smart glasses are, by nature, devices worn in close contact with the face for extended periods, demanding a much higher level of comfort than earbuds or smartwatches. Variations in nose bridge height, ear position, and skull width across different users mean that even minor discomfort can significantly reduce the desire for long-term use. Structural design must not only address module arrangement, but also seamlessly integrate with the human form.

Some brands have made practical improvements in this area. For example, XREAL introduced adjustable nose pads and multi-angle temple arms to better accommodate a wider range of users. Similarly, during the development of the TCL RayNeo series, the team conducted in-person fitting tests, collecting user feedback and continuously refining the nose support and temple curvature to strike a balance between comfort and stability.

The third challenge lies in the coordination between structure and interaction. 

In smart glasses, user interaction with the system often relies on eye-tracking, voice recognition, gesture control, or touch gestures—each of these interaction “entry points” must be physically embedded within the device’s structure. The placement of the camera affects the accuracy of image recognition; the layout of microphones and speakers impacts voice command recognition; and whether buttons or touch areas feel intuitive can directly influence how often certain features are used.

For example, Snap’s Spectacles 5 emphasizes a natural gesture-based experience by placing the capture button on the outer edge of the frame—right where the finger naturally rests—supporting the instinctive motion of “lift the glasses to look → press to capture.”

The fourth challenge—often the most underestimated—is whether the structure is suitable for mass production. 

Designing a concept pair of glasses is far easier than manufacturing one at scale. Critical factors include the maturity of mold design, whether the chosen materials support high-yield injection molding, whether CNC machining can ensure consistency, and whether assembly tolerances and production variances can be effectively controlled. These elements determine whether a product can move beyond the lab and successfully enter the market.

3. Muscle: Hardware Production & Supply Chain

If system design is the brain of smart glasses, and industrial design is the skeleton, then hardware production and the supply chain are the muscle system that keeps everything running. This "muscle" determines whether smart glasses can be mass-produced, whether they are reliable, and whether they can reach consumers at an acceptable cost.

At this stage, the question is no longer “Can we build it?” but rather: “Can we build it consistently, at scale, and with stability?”

Real-world challenges in smart glasses manufacturing

First, the level of integration and precision required for key modules is extremely high. A single pair of smart glasses typically includes a display, main processor, battery, microphones, speakers, cameras, sensors, and more—all of which must fit within a frame smaller than 150mm, while still ensuring comfortable wear. This places enormous demands on structural strength, thermal management, circuit layout, and material selection.

Traditional eyewear manufacturers often lack the technical expertise to meet these complex requirements, while many consumer electronics companies fall short in areas like comfort, wearability, and aesthetics. Very few companies truly possess strong system integration capabilities.

Supply chain integration: not just manufacturing, but collaboration

Production has never been a solo effort—especially for smart glasses, where module complexity and process diversity are immense. A single product may involve a dozen or more suppliers: injection molding, CNC machining, display modules, connectors, battery packs, lenses, coatings, electrochromic materials... The coordination of timelines and risk management across these stages is the true challenge of supply chain management.

Global collaboration has become the norm. The success of the Ray-Ban Meta smart glasses relied heavily on the cross-industry partnership between Meta and EssilorLuxottica: Meta led software and module development, while EssilorLuxottica handled the product design and traditional eyewear manufacturing. This type of strong partnership reduces trial-and-error costs and improves delivery certainty.

Mass production barriers: from “can be made” to “can be made reliably”

Even if a prototype is successfully built, the real challenges begin with mass production: How do you scale it? How do you improve the yield rate? How do you control costs without compromising user experience?

For instance, a custom Micro OLED display may cost hundreds of dollars, and even slight misalignment during assembly can ruin it. Internal wiring in the temples is prone to breakage during assembly, requiring specialized fixtures. The welding stability of battery modules directly impacts product safety. Even the compression resistance of packaging can affect compliance during shipping.

Therefore, the “muscle system” of smart glasses must include not just manufacturing capabilities, but mass production management. This is why companies with robust quality systems and automated manufacturing processes are more competitive in the smart glasses space.

To keep up with rapid evolution, muscle must respond fast

Lastly, the smart glasses supply chain must be flexible and adaptive. AI models are evolving quickly. Interaction methods are expanding from voice to eye-tracking and gesture control. Camera setups, chip platforms, and battery technologies are advancing rapidly. Meanwhile, consumer expectations for appearance, features, and battery life are constantly shifting. Whether the supply chain can adapt swiftly to these changes directly impacts a brand’s ability to iterate and stay competitive.

Conclusion

In the world of smart glasses, having a working prototype is just the beginning. The real test lies in turning that prototype into a reliable, comfortable, scalable product that people actually want to wear every day. That requires not only bold ideas, but also the ability to solve engineering bottlenecks, manage supply chain complexity, and iterate quickly with users in mind.

As the industry continues to evolve, teams that can integrate brain, skeleton, and muscle—software, design, and manufacturing—will be the ones to lead this new wave of computing.