AI-powered platform automating dental lab case management and fabrication workflows
EviSmart automates manual dental lab operations—case intake, data entry, scan QC—using an AI layer built on C# and .NET with Azure infrastructure. The hiring mix is engineering-heavy (9 of 18 roles), and active projects signal a shift toward operationalizing proprietary AI models and bridging the gap between ML capabilities and product surfaces. Pain-point data reveals the core friction: fragmented workflows and manual case management at scale, which the platform aims to solve through function-specific automation.
EviSmart is a dental lab software platform built to automate pre-fabrication tasks and case management. Founded in 2007 by a team with deep dental industry experience, the company serves dental laboratories looking to scale case volume without increasing manual overhead. The platform handles case intake, data entry, scan QC, and crown design automation. Operations span Canada and China, with 201–500 employees across engineering, product, design, and support functions. The stack is primarily C# and .NET on Azure, complemented by React frontends and Kubernetes orchestration.
EviSmart's core stack is C# and .NET, with React on the frontend. Infrastructure runs on Azure with Kubernetes and Docker for containerization, plus Terraform and Bicep for IaC. Tooling spans Azure DevOps, Jira, Confluence, and Zendesk.
Active projects include operationalizing proprietary AI models, building an AI layer for dental workflows, creating a command center dashboard, LLM integration into live systems, and automating internal processes. Leadership is also structuring onboarding templates and function-specific prompt libraries.
EviSmart™'s technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.