Richard Brinson

Applied AI specialist and founder with hands-on experience designing, building, and shipping production AI-powered software — from Generative AI and RAG-based assistants to full-stack SaaS platforms with automated workflows, compliance systems, and AI-driven decision support. Combines AI solutions architecture with end-to-end product ownership: backend infrastructure, billing and compliance automation, and AI-assisted operational tooling across healthcare, logistics, and business intelligence domains.

Founder & Applied AI Systems Architect at Rick Empire (Independent Multi-Venture Holding Company) (2019-03 – Present)

• Designed and built an AI-powered command-center dashboard (Empire OS) unifying venture tracking, task/document management, and decision support across a multi-business portfolio.
• Deployed an AI prompt engine generating automated weekly strategic analysis reports, applying Generative AI to real business decision-making.
• Architected and launched a HIPAA-compliant EHR SaaS platform (Cortex EHR) for mental health clinicians, including a Supabase backend, tiered subscription pricing, Stripe-driven billing, and an automated Business Associate Agreement (BAA) compliance workflow with AI-assisted note generation.
• Engineered an AI-scored logistics dispatch platform that aggregates freight and courier loads across multiple marketplaces and ranks them using an automated scoring algorithm (deadhead miles, rate-per-mile, equipment type, urgency).
• Led a full security audit of 39 serverless edge functions across the technology stack, identifying and remediating 8 critical vulnerabilities including exposed credentials and unsafe automation logic.
• Translated business requirements directly into deployed AI-first systems and automation pipelines, owning the full lifecycle from architecture through production deployment.

AI Solutions Project – RAG Business Assistant at Independent Strategy & AI Projects (2024-12 – Present)

• Built an AI-powered business assistant using Generative AI and Retrieval-Augmented Generation (RAG).
• Designed document-aware knowledge retrieval over structured and unstructured data.
• Implemented vector-based search for accurate, context-aware responses.
• Applied prompt engineering to improve reliability and output quality.
• Designed for scalable deployment using Azure-based AI services.

Applied Artificial Intelligence & Generative AI – Great Learning (2023 – 2025)