RESUME
Brooke Roney
AI Product Engineer | Real-Time Systems | AI Decision Interfaces
Summary
AI product engineer building real-time decision systems that combine live data, machine learning, and operator-focused interfaces. Proven track record of designing and shipping AI-native products across mobile and web, with systems that improve speed-to-insight, automate analysis, and drive measurable user outcomes.
Core Skills
Real-Time Data Processing
AI/LLM Integration (OpenAI, Claude, Whisper)
Decision System Design
Full-Stack Development (Supabase, APIs, Vercel)
iOS Development (Swift, StoreKit)
Operator Interface / UX Systems
Project
Sentinel
— Real-Time AI Decision System- ▸Built real-time system ingesting live inputs (video/audio/simulated events) to generate risk analysis and action recommendations with sub-second response time
- ▸Designed AI reasoning layer to classify events, assign confidence scores, and surface prioritized operator actions
- ▸Reduced time-to-decision from minutes to seconds through structured, actionable outputs
- ▸Developed operator-focused interface optimized for high-speed environments (clarity, hierarchy, zero-noise UI)
- ▸Integrated LLMs and streaming pipelines to simulate real-world monitoring and anomaly detection scenarios
Experience
- ▸Built AI-driven sales coaching system analyzing live conversations and delivering real-time feedback and scoring
- ▸Designed evaluation engine across structured performance criteria, improving coaching precision and consistency
- ▸Integrated speech-to-text + LLM pipelines to transform raw conversations into actionable insights within seconds
- ▸Connected live call data to CRM systems (Salesforce, HubSpot), enabling automated note generation and workflow triggers
- ▸Contributed to early growth to 1,000+ paid users across multiple organizations with ~$65–75/seat pricing
- ▸Built AI-driven personalization engine processing behavioral, contextual, and user-input data to generate dynamic insights
- ▸Designed data modeling system to create evolving user personas and adaptive recommendation layers
- ▸Implemented reinforcement loop improving relevance of recommendations over time based on user interaction signals
- ▸Delivered structured "Start / Stop / Shift" outputs, reducing cognitive load and increasing actionability of insights
- ▸Architected system to convert fragmented user data into continuous, real-time decision support
- ▸Built full-stack infrastructure for peptide research platform (frontend, backend, payments) with rapid deployment cycles
- ▸Designed data architecture for inventory, order management, and product tracking using Supabase
- ▸Implemented multi-rail payment systems (traditional + crypto), enabling flexible transaction handling
- ▸Structured workflows supporting compliance-oriented distribution (research-use labeling, controlled inventory)
- ▸Reduced deployment friction and iteration time through CI/CD pipeline using Vercel + GitHub
- ▸Designed AI-assisted production system enabling high-volume product design through agent-driven workflows
- ▸Built internal pipelines automating task intake → execution → iteration, reducing manual overhead
- ▸Increased output capacity per designer by leveraging parallelized AI-assisted workflows
- ▸Structured system connecting client inputs directly to production pipelines, minimizing latency
- ▸Advised companies on integrating AI into product strategy and internal tooling