I've been specializing in AI consultancy since 2025, building production systems that actually move the needle: - NXT Steel: Built order management automation projected to save $30k in 2025 & $106K in 2026 - Family Office: Developing contract analysis system saving ~$15K annually on vendor agreements My stack: - n8n (workflow automation) - Claude Code & API (AI integration) - Pinecone (vector databases) - Airtable, QuickBooks, Google Workspace - JavaScript (claude code) when needed I specialize in bringing AI into traditional industries - steel supply, family offices, businesses that need automation but don't have dedicated dev teams. See the family office case study in action: https://www.loom.com/share/b20a440d09544c65ae962f418712cbee What I'm looking for: - Part-time engagement (10-12 hrs/week) - Remote - Rate: $52.82/hr - Flexible on contract structure If you need someone who ships working systems (not just prototypes) and can integrate AI into existing business processes, let's talk. Drop a comment or DM me! ✍🏼

Hey everyone! Wanted to share something I've been working on that might help others delivering n8n projects. Complete AI Workflow Delivery Framework covering the entire process It's free and open for anyone to use. GitHub Repo: https://github.com/mjmirza/AI-Workflow-Delivery-Framework & Google Docs (if you prefer): - Master Checklist - Standard Operating Procedure - Client Onboarding Template - Security Audit Checklist - API Key Setup Guide - Maintenance Retainer Template Hope this helps someone out there. Feel free to fork, modify, or suggest improvements! Happy New Year 🎉

Hey you all! 🍄 Just finished putting together a case study on this document/order management system I built for a steel business I'm working with. It's been pretty wild - the thing is saving them 5+ hours daily and about $31k annually with a 4:1 ROI. I posted this in my other community but figured some of you might find it interesting to see how it all came together, so I threw together a Loom video walking through the whole process. Happy to share it as a free resource! https://www.loom.com/share/efeb0d336656438b9e44ffa257124bd3?sid=cfd98bd6-b349-4e62-a7df-11ab997c0559

🚀 Project Overview: Designing a contract analysis system for a multi-portfolio family office with 15,000+ vendor agreements spanning 200+ companies. Building an intelligent RAG system that needs to: - Auto-process contracts from existing Google Drive infrastructure - Stream new documents via N8N automation pipelines - Support complex legal queries across the full document corpus - Enable secure client access to the knowledge base 🛠️ Current Tech Stack: - Document Storage: Google Drive (client's existing setup) - Workflow Engine: N8N for document processing automation - Vector Database: Pinecone for semantic search capabilities - Parsing Engine: Evaluating Llama Index Cloud vs Dockling (on-premise) ⚖️ Architecture Decision: Favoring Llama Index Cloud due to superior legal document understanding and managed infrastructure, though client security policies may require on-premise parsing with Dockling. 📈 Scale Considerations: At 15K+ legal documents, I'm prioritizing architecture validation over rapid prototyping to avoid performance bottlenecks during production. 🎯 Seeking Community Input: 1. Pinecone Performance: Real-world experience with 10K+ document collections? Latency/cost insights? 2. Document Pipeline: Google Drive → N8N → Vector DB optimization strategies? 3. Legal Parsing: Comparative experiences with Llama Index vs Dockling on contract documents? 4. Access Control: Implementing secure multi-client access patterns with Pinecone? 🔧 Implementation Scope: Beyond vendor contracts, integrating internal policies and corporate agreements for comprehensive legal intelligence. Target use case: "Find all contracts with auto-renewal clauses expiring in Q3." 🧠 Learning from Veterans: If you were architecting a legal document RAG system today, what would be your non-negotiable design principles? Where did previous implementations hit unexpected complexity?

🎯 The Situation: Just wrapped a discovery call with a family office's general counsel - this could be my biggest project yet. Here's what I'm dealing with: 📊 Client Overview: - Mid-size family office (36 staff) - Managing 200+ investment vehicles - Processing ~10 vendor contracts weekly - 15K+ vendor relationships needing data cleanup - 45+ insurance policies with scattered renewal dates - Heavy manual processes across 7 departments ⚠️ Current Pain Points: - Legal counsel burning 30-60 min per contract review - Assistant playing telephone between departments, vendors, and C-suite - Using basic AI (Gemini) but everything downstream is manual - Clear workflow bottlenecks causing delays and frustration 💡 My Proposed Solution: Start with Phase 1 focused on vendor contract automation, integrating their existing stack (HubSpot, PandaDoc, Asana). Goal is workflow optimization, not just throwing more AI at the problem. 🤔 Where I Need Your Brain: 1. Scope Strategy: Better to laser-focus Phase 1 on vendor contracts only, or present a broader multi-phase roadmap covering their other workflow issues? 2. ROI Math: They won't share counsel's hourly rate. I'm thinking $400-500/hr industry benchmark for family office legal work - does that track? 3. Pricing Structure: Given their scale and $200K+ annual AI spend, should I stick with my usual approach or pivot to project-based pricing? 🛠️ Tech Stack Context: They're not tech-phobic - already using Gemini, HubSpot, PandaDoc, Asana, Google Drive (migrating to SharePoint). The foundation's there, just needs intelligent connecting. 💭 Looking for honest takes on strategy, pricing reality checks, or blind spots I might be missing. This could be a major breakthrough client but I want to nail the approach. What would you do?