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I designed and supported automated reporting workflows that moved data from source systems through validation and transformation into databases, spreadsheets, APIs, and reporting tools.
The goal was to replace repetitive manual steps with dependable, maintainable automation. My work included API integrations, field mapping, data validation, error handling, and delivery or refresh steps using tools such as Zapier, Google Apps Script, SQL, and Python. The workflow and code shown are sanitized portfolio examples with no client data, credentials, or proprietary automation logic.
The network for creativity
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Connect with clients, get discovered, and run your business 100% commission-free
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LumaClean was losing hours to manual quoting, scheduling, confirmations, rescheduling, and follow-up. LumaFlow turns that entire process into one automated booking workflow.
I built LumaFlow for a fictional Chicago cleaning business, LumaClean.
The problem was simple: too much back-and-forth just to turn a customer inquiry into a confirmed booking.
So I built a workflow that handles the journey:
→ Instant quote
→ Smart availability
→ $40 deposit
→ Booking confirmation
→ Reminders
→ Rescheduling
→ Booking recovery
The goal wasn't to build another pretty booking page.
It was to give the owner less work to do.
Vert nice
Another late night. Building a quant trading system an agentic model where different AI agents handle research, testing and risk, and none of them is allowed to place a trade on its own.
The hardest part so far? Being honest when the results say "not yet." Most strategies I've tested failed once real costs were included. That's exactly what testing is for.
Keeping The Psychology of Money and The Diary of a CEO close while I figure it out. What are you reading these days?
Curious how you’ve separated the agents from execution. When you say none can place a trade on its own, is that enforced through a separate execution service with fixed risk checks, human approval or both? I’d love to understand where the agent's decision-making ends and the hard rules take over.
I designed and built an enterprise AI automation system using n8n, AI agents, RAG, Redis, and PostgreSQL to automate complex business workflows, improve decision-making, and create scalable AI-powered operations.
The system uses n8n as the automation orchestration layer, where incoming business events trigger workflows that route tasks to specialized AI agents. A RAG pipeline retrieves relevant knowledge from business data sources, allowing AI agents to generate accurate, context-aware responses and decisions. Redis handles queue-based processing for high-volume tasks, while PostgreSQL stores structured data, workflow history, and audit records.
The automation architecture connects multiple technologies including n8n, OpenAI API, AI Agents, RAG pipelines, Vector Databases, Redis, PostgreSQL, APIs, Webhooks, Slack integrations, Docker, and Python services to create reliable enterprise workflows.
The solution helps businesses reduce manual operations, automate repetitive processes, improve response times, maintain better data accuracy, and scale AI workflows securely. It includes monitoring, validation, error handling, and human approval flows to ensure reliable production usage.
Tech Stack: n8n, OpenAI, AI Agents, RAG, LangChain, Redis, PostgreSQL, Qdrant, Python, FastAPI, Docker, APIs, Webhooks, Slack.
