AI / LLM Integration

AI integration goes far beyond calling an API. For LaunchThatBot, I built a platform where users can self-host OpenClaw AI agent instances on their own hardware and connect them to the central platform via WebRTC and WebSockets. The system supports multi-agent orchestration, RAG pipelines with vector search, real-time streaming responses, and a guardrails layer with input sanitization, PII redaction, and confidence-gated outputs. For AdaScout, I built an offline evaluation harness that measures AI scanner precision and recall against deterministic axe-core baselines, with per-criterion F1 tracking, LLM-as-judge validation for ambiguous findings, and regression gating on prompt changes. RAG over WCAG 2.2 AA specifications grounds remediation suggestions with specific criterion citations. Across projects, I've implemented confidence scoring, human-in-the-loop escalation patterns, and cost/performance trade-off analysis for model selection.