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Built 74 AI automation workflows from scratch with $0 budget. No team. No investors. Just n8n, Claude AI, and a Navy veteran's refusal to quit. Here's what's running in production for DustVault Technologies right now: competitor monitoring, federal contract tracking, compliance testing across 13+ states, verified contact finding via Hunter.io API, and a full Gmail intelligence system. MVP ships August 27. If you need AI automation built fast and right — let's talk.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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My core stack is less "generate pretty pictures" and more "build the pipeline that does the thinking":
n8n — the glue. Great for orchestrating multi-step agent workflows and connecting APIs without writing a service from scratch. Weakness: gets messy fast on complex branching logic, and debugging a broken node chain eats time.
RAG (usually with a vector DB + LangChain-style retrieval) — lets me ground LLM output in actual client data instead of hallucinated generalities. Strength: dramatically improves accuracy for domain-specific asks. Weakness: chunking/retrieval quality is finicky, and a bad embedding setup silently tanks results without throwing an error.
Claude/GPT for reasoning + code — my default for anything needing judgment: analysis writeups, SQL/Python generation, proposal drafting. Strength: fast iteration. Weakness: still needs a human check on numbers — confident-sounding ≠ correct.
Power BI / Tableau — where the AI output actually becomes something a client trusts. AI can shortcut the analysis, but the dashboard is what gets signed off on.
The honest lesson: the "AI" part is maybe 30% of the workflow. The rest is validation, data plumbing, and making the output legible to a non-technical stakeholder.
Silent failures are what I watch closest. I treat an answer as untrusted unless I can show exactly which source text the model read, because fluent output can hide a bad match.
Pipeline view of an agentic fundraising prototype for an AI client built in Framer. Rendered in Ultramock.
I had Claude build out ~800 rows (~50 columns) of dummy contact data in supabase with varying degrees of lead enrichment and match scoring to simulate a live DB connection.
Really enjoying using Claude x Framer. Its truly a superpower for building simulation engines like this.
800 rows of dummy data for a proto, madness
I'm building AI-powered automation systems that connect APIs, workflows, and intelligent agents.