Glenn D. House Sr.

DecisionMe Risk-abated Decision, Analysis, and Resolution (raDAR) is an AI-powered tool that helps individuals and teams make complex decisions with clarity and confidence. It guides users through a structured process: defining the decision, identifying key criteria, assigning importance to each factor, and comparing options to make informed decisions. The app runs "what-if" simulations and visualizes results through clear charts and reports, making choices transparent and data-driven.

Key features include AI-guided decision steps, customizable weighting, scenario modeling, collaboration tools, and detailed report generation. It works across devices and helps reduce bias by focusing on facts rather than emotions.

Benefits include faster decision-making, stronger confidence, reduced stress, and better team alignment. DecisionMe raDAR handles complex, multi-criteria choices easily—whether personal or business—and provides clear explanations for every outcome. In short, DecisionMe raDAR transforms decision-making into a smarter, more objective, and stress-free experience.

Headed the development of a highly parallelized supply chain simulation to enhance the operational viability of the F-35 program. I directed a cross-functional effort integrating advanced modeling, data analytics, and high-performance computing to optimize logistics efficiency and resource utilization across global sustainment operations. The implemented Monte Carlo simulations across 50,000+ critical parts forecast demand variability and supplier performance, improving sustainment accuracy to within 4.5% of actual demand.

The initiative accelerated sustainment planning cycles, reduced lifecycle costs, and strengthened readiness outcomes across partner nations. I designed a dynamic visualization platform to present model outputs and scenario-based insights to executive stakeholders, enabling informed, data-driven decisions under uncertainty.

The results influenced long-term procurement and readiness strategies, demonstrating the power of predictive analytics and digital simulation to modernize defense logistics and drive measurable operational resilience.

Spearheaded the creation and digital transformation of the supply chain software center of excellence, securing $12 million in strategic funding and tripling AI and advanced analytics adoption across enterprise programs.

I directed cross-functional teams to design and execute a comprehensive digital roadmap, aligning technology investments with mission-critical sustainment goals. I also established governance frameworks to ensure the ethical and responsible deployment of AI, enabling scalable and transparent decision-making. Long-term strategies for AI integration, predictive logistics, and operational automation were developed, resulting in a 98% readiness rate for special equipment mission aircraft worldwide. Finally, I partnered with engineering, IT, and program leadership to define data-driven transformation objectives, implement key performance dashboards, and prioritize high-impact initiatives.

This effort positioned the organization as a leader in digital sustainment innovation, accelerating operational agility and optimizing lifecycle cost management across global defense operations.

decisionMe raDAR is an AI-powered decision-support platform developed by decisionMe, Inc., a company based in Westborough, Massachusetts. The name raDAR stands for risk-abated decision, analysis, and resolution. It is designed to help individuals and organizations make complex choices more confidently by combining data, structure, and human insight.

Organizations are adopting large language models to improve decision support, yet reliability and reproducibility remain challenges. decisionMe has built an internal platform that solves these issues by enabling real-time development, testing, and deployment of AI prompt templates directly within live applications. The system enables subject matter experts to iteratively refine prompts and prompt chains, compare performance across multiple AI models, and validate them through dynamic testing to ensure stability, guardrail compliance, and resilience under varying conditions. By allowing SMEs to capture domain expertise without coding, the platform reduces dependence on engineers and anchors AI behavior in real operational knowledge. Once validated, prompts and chains can be published into decisionMe applications, creating a continuous loop between experimentation and execution. This environment enhances decision quality by aligning AI behavior with organizational objectives, facilitating benchmarking across models, and enabling adaptive, trustworthy, and decision-centric AI.