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Owned by Duy

AI Automation First Client

232 members β€’ Free

From zero to first $1k/month with AI automation in 30 days. Get the exact formula + templates that landed 100+ their first client.

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62 contributions to AI Automation First Client
Built My First Template Library (Now Earning While I Sleep)
Stopped building from scratch 6 months ago. Started using templates. Now earning $3,400/month on autopilot. THE TEMPLATE BREAKTHROUGH: Realized I was rebuilding the same workflows. Dental forms processing: Built 7 times Invoice extraction: Built 12 times Contract parsing: Built 9 times Application processing: Built 6 times Same automation, different field mapping. THE LIBRARY I CREATED: HEALTHCARE TEMPLATES: - Patient intake forms (used 15 times) - Insurance verification (used 8 times) - Medical records transfer (used 6 times) - Billing automation (used 11 times) BUSINESS TEMPLATES: - Invoice processing (used 23 times) - Contract data extraction (used 14 times) - Purchase order automation (used 9 times) - Expense report parsing (used 7 times) Each template = 15 minutes to deploy Each deployment = $1,500-2,500 revenue THE TEMPLATE ECONOMICS: Development time per template: 8-12 hours Uses per template: 6-23 times Revenue per use: $1,500-2,500 Total template ROI: 1,200-2,800% MY TOP PERFORMING TEMPLATE: "Invoice to QuickBooks Automation" Built once: 8 hours Deployed: 23 times Average price: $1,800 Total revenue: $41,400 Time invested after initial build: 6 hours ROI: 6,900% THE TEMPLATE COMPONENTS: Base automation workflow Field mapping variables Error handling protocols Client notification systems Integration options THE TOOLS POWERING MY TEMPLATES: PDF Vector: Document parsing engine Make.com: Workflow automation Google Sheets: Data transformation Various APIs: System integrations Example template flow: Document upload β†’ PDF Vector parsing β†’ Data validation β†’ System integration β†’ Client notification THE DEPLOYMENT PROCESS: Client consultation (30 minutes) Template selection (5 minutes) Field mapping (10 minutes) Testing (5 minutes) Go-live (immediate) Total time: 50 minutes THE SCALING EFFECT: Month 1: Built 3 templates, deployed 3 times Month 2: Built 2 templates, deployed 8 times Month 3: Built 1 template, deployed 12 times
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Project Knowledge chatbot
Hey all, Any recommendations/experience for following usecase? I got a Client who asked for a RAG chatbot for his construction employees. They want to ask the chat bot for execution details instead of looking through many documents manually. I saw there are new solutions possible using knowledge graph. Do you prefer simple rag (+ reranking?) or knowledge graph? Thank you πŸ™πŸ»πŸ™πŸ»
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0 likes β€’ 47m
For construction execution details, start with simple RAG + reranking. Knowledge graphs are overkill initially. Why simple RAG works for construction: - Execution details are usually procedural (step-by-step) - Documents have clear structure (specs, SOPs, safety protocols) - Questions are specific ("How to install X?" "What's the torque spec?") - Users need fast, accurate answers, not relationships Best setup: 1. Document processing: PDF Vector to extract all construction docs 2. Chunking: Split by sections/procedures (not arbitrary tokens) 3. Embeddings: OpenAI or local model 4. Vector DB: Pinecone/Weaviate 5. Reranking: Cohere or cross-encoder 6. Safety layer: Flag anything requiring supervisor approval Critical for construction: - Include document source/version in responses - Highlight safety warnings - Show relevant diagrams/images - Track which docs employees access most Start simple: Week 1: Get basic RAG working Week 2: Add reranking for accuracy Week 3: Fine-tune based on actual queries Month 2: Consider knowledge graph if needed
How do you approach information privacy when using AI to scan/search documents?
Hi all - I've been going through multiple rabbit holes for most of the day searching, reading through and reviewing AI platforms and APIs. The Issue: I'm looking to see how to prevent/eliminate data privacy issues when using AI/AI APIs to scan and/or search documents from folders and/or email to determine a specific action. Though there are platforms that offer zero data retention agreements, they're typically only reserved for "higher end" or "enterprise" plans where a company negotiates it into their plan/contract. How are any of you preventing/approaching/handling PII (aka personally identifying information) from AI platforms storing this type of information for processing in highly regulated industries?
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1 like β€’ 50m
Technical approaches: 1. Pre-process locally - Strip PII before API calls - Replace with tokens - Reinsert after processing 2. Self-hosted - Run models locally (Ollama) - On-premise n8n - No external APIs 3. Zero-retention APIs - PDF Vector (no storage) - Azure OpenAI (enterprise) - Anthropic (30-day deletion) Healthcare setup: 1. Identify PII locally 2. Redact and save mapping 3. Send redacted to API 4. Get results, restore PII 5. Delete temp files Business solution: Get BAA/DPA agreements - available at $500+/month tiers now, not just enterprise.
Day 6 of 30
Goal: - Find and reach out to another 10 prospects. - Create a new, simple automation. Blocker: Nothing at the moment Need: Nothing at the moment
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1 like β€’ 11h
πŸ’―
My 30 Day Challenge
It's time to make a public commitment. I will be posting my daily tasks and achievements here on this platform. I know it's not a sprint but I'm hoping to build tangible business solutions within this period. Day 1 General Setup Get my template and accounts ready I.e n8n, PDF Vector, loom, and PandaDoc. I already have make.com and Calendly accounts. The plan is to have all of the needed tools in place at the end of Day1 Blocker - None at the moment
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1 like β€’ 11h
πŸ’― So good man, keep doing it
1-10 of 62
Duy Bui
5
126points to level up
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@duy-bui-6828
Built automation systems doing 20K+/mo. Now helping automation builders get first clients FREE. No courses, just action

Active 11m ago
Joined Sep 7, 2025
Ho Chi Minh City