Custom software and AI-enabled systems for enterprise modernization
Aezion is a Dallas-based custom software firm expanding into AI infrastructure. The tech stack reveals a deliberate shift toward ML ops: TensorFlow, PyTorch, scikit-learn, Keras, and vector databases (Pinecore, Qdrant, pgvector) sit alongside traditional .NET and cloud (Azure, AWS). Active projects—fraud detection, generative AI deployment, and database-as-code—paired with pain points around model performance and legacy data migration—signal a pivot from pure dev services toward AI systems integration. All five current hires are senior-level roles in engineering, suggesting they're building specialized capability rather than scaling commoditized services.
Aezion provides custom software development, mobile apps, and web applications to mid-market clients across the Dallas-Fort Worth region and beyond. The company was founded in 2012 and operates as a privately held firm of 51–200 employees. Beyond traditional dev services, Aezion now offers hosting, support, business intelligence, and digital marketing. The service model spans the full lifecycle: architecture through deployment and ongoing maintenance. Notable specialization areas include healthcare software and QA/testing.
Aezion develops in .NET (C#, ASP.NET, MVC), React, Angular, Vue, and JavaScript on the frontend; SQL Server and Azure/AWS in the cloud. Recent additions include ML frameworks (TensorFlow, PyTorch, Keras), vector databases (Pinecone, Qdrant, pgvector), and LangChain for generative AI integration.
Frisco, Texas. The company serves clients across the Dallas-Fort Worth metroplex and operates as a privately held custom software provider founded in 2012.
Aezion, Inc'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.