Federal analytics and IT modernization for law enforcement and Homeland Security
AVER is a government-focused IT modernization firm serving federal agencies in Homeland Security, law enforcement, and biometrics. The tech stack reveals heavy reliance on Microsoft (Azure, Azure Government Cloud, Power BI, ServiceNow) paired with Python ML tooling (scikit-learn, TensorFlow, PyTorch, XGBoost), signaling modernization of legacy analytical systems. Active projects confirm this direction: financial system modernization, migration from Oracle to Azure Government Cloud, and biometric data pipeline work. The hiring mix—security-heavy (4 roles) and senior-weighted (9 of 15 open roles)—reflects both compliance-first government requirements and a search for experienced architects to lead system replacements.
AVER is a Service-Disabled Veteran Owned Small Business (SDVOSB) delivering analytics and modernization services to U.S. federal government agencies. The firm focuses on advanced analytics, IT modernization, and mission-critical biometric services for federal customers in Homeland Security, law enforcement, and healthcare. With 51–200 employees based in Washington, D.C., AVER operates at the intersection of legacy system replacement and emerging cloud and ML platforms. Core operational challenges include scaling biometric database search, identity resolution, and maintaining 24/7 availability for mission-critical systems while transitioning from on-premise (Oracle, SAP) to cloud infrastructure (Azure Government Cloud).
AVER uses Microsoft Azure, Azure Government Cloud, Kubernetes, Python (FastAPI, Flask, Django), React, Power BI, Tableau, Oracle, AWS, Docker, ServiceNow, and ML frameworks (scikit-learn, TensorFlow, PyTorch, XGBoost). The firm is migrating from SAP and PRISM to modern cloud and analytics platforms.
AVER is headquartered in Washington, District of Columbia, and focuses exclusively on U.S. federal government clients.
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AVER, LLC'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 →
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