e-Title and registration platform for auto dealerships and lenders
DLRdmv® operates a specialized SaaS platform for automotive title and registration processing across multi-state jurisdictions. The tech stack—C#, MVC, Angular on Azure and AWS with SQL Server and PostgreSQL—reflects a mature, regulated-industry application with dual-cloud infrastructure. Active projects signal infrastructure modernization (on-prem to cloud migrations, reusable ETL frameworks) paired with AI expansion (Copilot Studio agents, platform evaluation), while hiring velocity is accelerating across engineering and sales, indicating both product scaling and go-to-market expansion.
DLRdmv® provides end-to-end e-title and registration software for auto dealerships and financial institutions managing high-volume multi-state transactions. The platform automates the entire workflow from title generation through DMV submission, with compliance and accuracy as core requirements. Founded in 2016 and headquartered in Dallas, the company serves dealerships nationwide and operates state-specific integrations (Florida EFS, Georgia ETR, Minnesota EVTR, Wisconsin e-Title). The current product roadmap includes next-generation etitling software, mission-critical reporting systems, and new software packages, alongside infrastructure work to migrate legacy systems to cloud and implement AI governance frameworks.
DLRdmv uses C#, MVC, Angular, and SQL Server as core layers, deployed on Azure and AWS with RDS and PostgreSQL. Data and automation work runs on Apache Airflow, Power Platform, and Snowflake. DevOps and CI/CD leverage Azure DevOps and AWS Systems Manager.
Active projects include next-generation etitling software, large-scale cloud migrations (on-prem to cloud, cross-database), reusable ETL frameworks, AI platform rollout with Copilot Studio agents, and multi-state government affairs strategy.
DLRdmv®'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.