Lawrence Neale

Led the architecture and delivery of a large-scale GenAI clinical platform, multi-tenant and event-driven microservices on AWS, supporting 200+ concurrent voice and text AI sessions. Built a real-time voice AI pipeline converting clinical conversations into structured medical entities with ~1s latency and ~97% extraction accuracy, enabling automated documentation workflows. Designed and deployed production RAG systems using LangChain and vector databases (Pinecone/FAISS), achieving 0.9 retrieval precision on patient-history summation. Developed a modular LLM orchestration layer separating retrieval, prompt execution, and inference services for safer prompts and rapid feature delivery. Implemented AI reliability strategies (retrieval validation, structured output enforcement, evaluation feedback loops) to reduce model inconsistency in clinical outputs. Integrated GraphQL APIs and WebSocket streaming with React interfaces for live monitoring and real-time summaries, and reduced frontend

Built real-time analytics platforms, ML inference systems, and data engineering infrastructure. Operated streaming ingestion platforms processing 3,000+ events/second using Kafka, AWS Kinesis, and Azure Event Hubs. Maintained a centralized PostgreSQL data warehouse with Redis caching, achieving ~200ms p95 analytical query latency. Developed React + D3.js analytics dashboards enabling 50+ stakeholders to visualize clinical and operational insights in real time. Applied Bayesian optimization models improving clinical trial site selection efficiency by ~18%. Integrated ML inference services into analytics systems using containerized FastAPI deployments on AWS ECS and Azure AKS. Built predictive models for radiological analysis and equipment failure forecasting, reducing operational downtime by ~28%.

Founded and engineered an AI-driven lead generation and customer acquisition platform, growing from £0 to £100k ARR. Built a 59-node production n8n OSINT enrichment pipeline integrating 12 APIs in parallel (LinkedIn, Twitter, Facebook, Reddit, Whois, B2B data enrichment, email verification) to cut manual research time from 8+ hours to under 15 minutes per subject. Created an AI Intelligence Platform with 11 parallel data-gathering agents and a Next.js dashboard showing live agent status, risk-rated reports, and structured intelligence briefs for security agencies and B2B sales teams. Implemented a four-module automated lead generation pipeline (Maps scraping → Tavily AI research → ICP scoring → AnyMailFinder CEO email discovery) and an outbound engine with ICP-matched multi-touch sequences (email + LinkedIn) delivering higher reply rates and faster pipeline velocity. Deployed a multi-agent Telegram AI system with 8 specialized sub-agents and built a customer retention automatio