Systems

Selected production AI systems I've designed and built.

AI PR Generation System

Problem: Manual PR authoring was the primary bottleneck across the engineering org — slow cycle times, inconsistent quality, high context-switching cost.
System: End-to-end pipeline from user story to production-ready PR: code generation, test/lint execution, and automated review gating.
Control: Execution-time evals at each stage. Feedback from review failures loops back into prompt and system tuning — a closed control loop, not a one-shot pipeline.
Outcome: ~3x increase in throughput to production. PR authoring time reduced by 93%, code review time by 80%.

LLMOpsRAGMCPEvalsDevOpsCursorAider

Problem: Large-document ingestion was unreliable — failures were silent, partial ingestion corrupted retrieval quality, and there was no visibility into pipeline state.
System: Queue-based chunking with distributed workers and dead-letter queues. Designed for long-document ingestion into knowledge systems used by retrieval pipelines.
Control: Data validation gates at ingestion boundaries. Retrieval evals measure downstream quality — ingestion failures surface as retrieval regressions, not silent corruption.
Outcome: Stable, observable ingestion for large-scale knowledge systems. Eliminated silent failures and improved retrieval grounding quality.

AI PlatformRAGDistributed SystemsQueue ArchitectureEvals

Problem: Agent workflows were opaque — no way to trace execution, intervene mid-task, or debug failures after the fact.
System: Real-time supervision interface for AI agent execution (OpenCode). Server management, streaming chat, terminal emulation, git viewer, and file annotations. 37+ PRs shipped in 2 weeks.
Control: Execution tracing across every layer. Human-in-the-loop checkpoints for approval, redirection, and abort. Full session replay for post-hoc debugging.
Outcome: Debuggable, controllable agent workflows. Engineers can observe, intervene, and approve agent execution in real time.

AI Agent SupervisionReact NativeTypeScriptObservabilityMobile

Problem: A Windows-only desktop application needed to reach 5 platforms without fragmenting the engineering team or codebase.
System: Single React Native codebase serving macOS, Linux, Windows, iOS, and Android. CI/CD automation, OTA updates, and platform-specific build infrastructure for a 25-engineer org.
Control: Automated release pipelines with platform-specific validation gates. Deployment infrastructure reduced release friction by 98%.
Outcome: Release cadence increased 300%. Platform coverage expanded from 1 to 5 with no team fragmentation.

Platform EngineeringReact NativeTypeScriptC#/.NETCI/CDArchitecture