The Blueprint: How Movix fills a gap in dental skills with specialized agentic AI

Welcome to The Blueprint, a regular feature where we highlight how Google Cloud customers are tackling unique and common challenges across industries using the latest AI and cloud technologies. We hope to inspire others looking to innovate in their work.

The demand for dental appliances, like crowns and aligners, is booming, but it’s hard for manufacturers to keep up. At Movix, we’re building one of the first agentic AI solutions for dental appliance manufacturers and dental labs to help companies in the sector acquire digital technical expertise so they can scale clinical workflows cost-effectively and consistently. 

The challenge:

Movix started in 2025 with a mission to solve a serious shortage of skilled dental technicians in aligner manufacturing through AI and agentic workflows. The need is significant: the global dental market is valued at nearly $400 billion and growing at double digits, yet many operations remain analog – creating enormous demand for co-pilot, agentic solutions.

Before founding Movix, we had previously started a vertically integrated dental aligner company that focused on very difficult dental situations, such as very crooked teeth. Yet even with highly skilled and trained technicians, there were often mistakes that would require remaking an aligner — a process that costs $300, roughly 25% of the retail price. Poor quality control took a real bite out of the company’s margins.

We saw an opportunity with Movix to address these mistakes by providing technicians with AI-powered quality control agents that automate aligner workflows and reduce errors. To achieve this, we needed to solve for a few key technical challenges:

• Develop a custom AI model and end-to-end agentic workflow, since off-the-shelf solutions lacked domain expertise, 

• Ensure scalability would be built into the platform to prevent outages or production delays,

• Achieve broad interoperability through a complex hybrid integration strategy since many dental practices are slow to adopt new technology and run on legacy systems.

• Optimize security and compliance to comply with medical record regulatory requirements and keep patient data safe. 

The solution:

In order to deliver AI agents that can provide expert-level accuracy, we needed to custom build a lot of the tooling ourselves. We started by developing our custom models for deep learning, computer vision, and 3D mesh analysis over a five-month period, using Google Cloud infrastructure. This intensive, methodical time helped ensure the right level of accuracy and quality control. 

We use Google Cloud infrastructure across the full pipeline — from dataset storage and model training to evaluation — to build and refine our defect detection models for intraoral scans. Once defects are detected, we use Gemini Enterprise Agent Platform to generate client-facing feedback that reads as if it came directly from a human technician — acting as a digital team member in the quality control workflow.

Our 3D models use Cloud Run with L4 GPUs for the massive compute power we require; notably, performing the 3D segment scans and detecting defects across the entire fabrication process are highly compute-intensive processes. We use Compute Engine VMs to run experiments, along with various other GPUs to train our models, and perform the heavy lifting of model development in this environment. 

Cloud Run and other tools like Cloud Storage support our scalability goals as we target large customers who handle high case volumes — some large labs might produce up to 200,000 appliances per year. Google Cloud’s global network of data centers also simplifies regulatory compliance across regions and ensures fast delivery of large 3D datasets to clients worldwide.

The architecture:

The outcome:

Our agentic solutions automate data entry and quality control, which are traditionally manual, time-consuming, and error prone tasks. By automating the work of the best dental technicians, we’re ensuring a top quality product that will improve the fit of crowns, aligners, veneers, and implants for many, many patients. We estimate that our automation and the higher level of accuracy our QC agent delivers could save an aligner manufacturer $300 per remake, for example. 

We also believe we’re helping to speed the appliance manufacturing process, leading to quicker turnaround times for dental appliances, which helps dental labs receive revenue faster and improve their cash flow. And we already know we’re meeting a critical need: After we launched the QC agent in October 2025, our first customer signed with us in December. That customer, Orthero, an aligner company serving more than 20 countries, has enjoyed significant results.

“Orthero benefits from this automation by making quality control faster, more consistent, and scalable,” Efer Turhan, a co-founder of Orthero, said. “With support from Movix’s QC AI Agent, we detect missing or inconsistent inputs early and flag unusual deviations before they cause delays.” 

The details:

Even with the advantages of AI, our goals demand some serious work. Our architecture supports a solution that’s agentic and modular, integrates into existing on-premises dental systems, and ensures security and compliance.

Our agentic approach allows our system to run checks and balances, manage the complex, multi-step process of quality control for dental scans, and eliminate human errors that occur in data handling and quality review. Our goal is to develop five distinct AI agents by 2029 that cover the entire dental appliance workflow, from original patient dental scan to appliance manufacturing. While our first agents focus on data entry and dental scan quality control, our next agents will handle 3D file repair, clinical review, treatment planning, and manufacturing.

Our solution architecture also enables our system to integrate seamlessly with our customers’ existing lab management and manufacturing systems through API integrations. Because we are selling our solution into a conservative market, we decided to bear the burden of responsibility for successful adoption by doing as much of the integration work as possible.

Because we operate in the highly regulated healthcare industry, we built an environment that strictly follows compliance rules, anonymizing protected health information, or PHI, before it enters our machine learning pipeline to prevent health information from being exposed to the processing environment.

We plan to build hybrid solutions to capture a wider market as we move forward. We’re designing an architecture that connects our cloud-based AI agents with older, on-premises software that many conservative labs still use — through lightweight local connectors and standardized APIs. This will allow us to access a large market segment that has not yet migrated to the cloud or begun to use new digital dental technologies.

Taken together, we are not just solving a skills gap, we are reimagining what is possible  with co-pilot and agentic solutions across the entire dental industry.