Technical deep dives on the systems, automations, and architectures that keep SaaS and AI products running.
Capital Systems run in logically isolated environments (VPCs). Your documents and logs stay inside your environment and are never pooled into a shared corpus.
Your IP is not used to train public foundation models. No prompts, documents, or outputs are sent back for model training unless explicitly opted in.
Answers are grounded in your docs with inline citations. If it's not in your corpus, the system returns a clear “not found in your documents” response.
You own your data, embeddings, and vector indexes. Built on open standards so you can move your stack—no hard lock-in or black boxes.
How I deployed Vertex AI Search/RAG over internal SOPs and tickets, wired a retrieval agent into Slack, and measured the drop in “where do I find X?” questions.
Stop forwarding emails manually. Learn how to build an AI router that tags, prioritizes, and drafts replies for support tickets.
Stop overpaying for rigid tools. The 2026 stack is modular: HubSpot + n8n + Supabase + OpenAI. Here is the architecture guide.
A deep dive into using n8n, OpenAI, and Postgres to score leads before they hit your CRM. Reduce sales rep wasted time by 90%.
Data entry is a script's job. How to replace your $500/mo VA with a robust automation pipeline using Regex and LLMs.
Stop spamming. Learn how to build an AI agent that detects "Out of Office" vs "Interested" replies using GPT-4 and Webhooks.
Marketing isn't magic, it's a search algorithm. How to use search operators to find high-intent leads programmatically.
Don't type data manually. Snap a photo of an invoice or receipt, and let Vision AI update your database instantly.
Zapier is great for prototypes, but terrible for scale. The technical breakdown of when to switch to n8n or Node.js.
I build these exact systems for clients in 7-day sprints.
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