Can I create a unified AI character to build a channel for a brand, a cross-brand? The character must be able to use the language of our country (Vietnam)? If I have it, my work will be much easier than having to use prompt directly on Gemini Google Veo3. I look forward to receiving your help.

Client called Friday at 3 PM. Emergency. 🚨 ​ "Our medical billing outsourcer just quit. We have 400 patient superbills sitting in email. We need to process them by Monday for insurance claims. Can you help?" ​ I said yes before fully thinking it through. ​ THE SITUATION: 🏥 Healthcare provider, 400 PDFs in Gmail, each needs specific data extracted (patient info, procedure codes, diagnosis codes, charges) and entered into their billing system. ​ Manually: 400 documents × 8 minutes each = 53 hours They had 48 hours before Monday deadline Doing it manually: Impossible 🤯 ​ THE SOLUTION I BUILT: 🚀 I opened n8n. Started screen recording. Built it live on Zoom while they watched. ​ WORKFLOW (6 nodes): 1. Gmail Trigger - monitors label "billing-superbills" 📧 2. PDF Vector Parse Document - converts superbill to clean text 📄 3. PDF Vector Extract Structured Document - pulls patient data, CPT codes, ICD-10 codes, charges using JSON Schema 📊 4. Format data node - maps to their billing system format 5. HTTP Request - posts to their API 📤 6. Gmail - moves processed email to "completed" folder ✅ ​ Build time: 47 minutes (including testing with 5 sample superbills) Accuracy: 98% on field extraction (they manually verified random samples) Processing time: 400 documents in 2.3 hours ​ MY PRICING: 💰 - Emergency setup: $900 (normally $1,200 but quick simple workflow) - Monthly cost: $0 (they only use it occasionally, running on n8n Cloud free tier initially) - Their avoided cost: Would've paid overtime staff $2,800 for weekend emergency processing ​ They were stunned. "We thought this would take days to set up." 😮 ​ KEY LESSON: 🔑 Document processing workflows are FAST to build when you have the right tools. The JSON Schema approach lets you specify exactly what fields you need extracted. PDF Vector handles the messy parsing and OCR. ​ ACTUAL JSON SCHEMA I USED (simplified): 📋 { "patient_name": "string", "patient_dob": "string", "cpt_codes": "array", "icd10_codes": "array",