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I build production AI and automation systems. My work connects agents, APIs, and business operations across multi-agent orchestration, n8n workflows, MCP servers, developer productivity tools, and full-stack applications—with a focus on clear interfaces, bounded actions, and systems people can understand and operate.
I’m interested in AI Automation Engineer, Solutions Engineer, and Developer Productivity Engineer roles. I bring product-minded engineering from problem framing through implementation, integration, and documentation.
Core strengths: agent and workflow architecture · REST APIs and webhooks · MCP tool interfaces · human-in-the-loop controls · secure browser tooling · service integration · CI/CD · technical documentation.
| Project | Overview | Stack / focus |
|---|---|---|
| Lead Automation Platform Case Study | Sanitized case study of event-driven lead capture, payment verification, CRM enrichment, outreach, and operations reporting. | Cloudflare Workers · Python · REST APIs · Webhooks |
| AI Operations Center | Small-business observability concept organized around fleet health, cost intelligence, incident response, governance, business metrics, and agent registry. | Python · TypeScript · APIs · Observability |
| Robot Orchestra | Local-first Windows agent copilot with a draggable robot head, persistent Brain and Knowledge, Ollama/Hermes routing, browser scanning and optional speech-to-speech. Download 0.6.0. | Electron · React/TypeScript · .NET Native Messaging · Ollama · Chrome/Edge |
| GHL Pro MCP | MCP server connecting AI tools to GoHighLevel CRM workflows, lead scoring, and analytics. | MCP · APIs · CRM |
| API Scout | Local-first catalog and CLI for discovering free APIs and free-tier LLM providers. | Developer tooling · APIs · local-first |
| n8n Automations | EDGE / SEARCHER API and curated n8n workflow resources for search and automation patterns. | n8n · API integration · observability |
| ModelDock USB | Local LLM agent operations platform with human oversight. | Local AI · agent operations · developer tools |
| BrainScan | Auto-scanning second brain: watches your documents, answers with citations, or refuses. | Python · Qdrant · Docling · FastAPI · RAG |
| Clockwork | AI employee framework: clocks in on its own, gathers context, memory and skills, waits for approval, executes, checks, and remembers. | Python · CLI · SQLite · Agent framework |
I favor clear boundaries over opaque “magic”: an explicit entry point, a scoped orchestrator, narrowly defined agent and tool capabilities, and an approval checkpoint where actions need human judgment. Integrations use stable contracts, and logs capture outcomes for debugging and handoff.
Architecture source (Mermaid)
flowchart LR
subgraph INPUT["01 · Entry points"]
EVT[User request / event]
UI[App / browser extension]
WH[Webhook / schedule]
end
subgraph CONTROL["02 · Orchestration"]
API[Python / TypeScript service]
N8N[n8n workflow]
GATE{Approval gate}
end
subgraph INTEL["03 · AI & tools"]
AGENT[Scoped AI agent]
MCP[MCP server]
APIX[External APIs]
end
subgraph OPS["04 · Operations"]
SYS[Business systems]
OBS[Logs · outcomes · retries]
end
EVT --> API
UI --> API
WH --> N8N
API <--> N8N
API --> AGENT
N8N --> AGENT
AGENT --> MCP
MCP --> APIX
APIX --> SYS
AGENT --> GATE
GATE -->|Approved| APIX
GATE -->|Review| EVT
API --> OBS
N8N --> OBS
APIX --> OBS
- n8n workflows: clone the portfolio, select a workflow under
workflows/, import its JSON into n8n, configure the required credentials in n8n’s credential manager, then test with representative data before activation. - Flagship automations: clone EDGE-AGENCYAutomations-n8n, choose a documented workflow, import the JSON, configure only its required integrations, and validate triggers and approval steps before enabling production runs.
- Agent service: use the JARVIS Department Agent README for environment setup and local run instructions; it documents the service-specific requirements.
Keep secrets in environment configuration or the platform’s credential store—never in committed workflow exports. Validate API permissions, failure handling, and review gates in a test environment before connecting live systems. Project-specific runtime and deployment instructions remain authoritative in each repository.
Inspectable by default. I aim to make capabilities, assumptions, and system boundaries visible. Practical delivery means a reproducible setup, scoped integrations, useful error handling, clear docs, and appropriate human review—not just an impressive demo.
EDGE | AGENCY · Practical AI systems, thoughtful automation, better developer workflows.