ALL WORK / amanda-enterprise
In production2025● FEATURED

Amanda Enterprise

Amanda Enterprise is a fully automated, enterprise-grade client lifecycle system built entirely in n8n.

n8nTypeScriptGPT-4Claude APIGoHighLevelSupabase
ROLE
Automation Architect & n8n Workflow Engineer
PLATFORM
n8n (Self-Hosted / Cloud Compatible)
YEAR
2025
IMPACT
Full client lifecycle automation
THE PROBLEM

Multiple client teams juggling ClickUp, Airtable, Supabase, and GoHighLevel — every task classification, routing decision, and status update was manual. Ops overhead compounded per client.

THE APPROACH

One 190-node n8n orchestration layer sitting between all four platforms. LLM-driven task classification, autonomous routing, real-time cross-system syncs. Custom TypeScript nodes for platform-specific logic. Multi-tenant from day one.

WORKFLOW · 7 NODES
WebhookClassifyRouteClickUpAirtableSupabaseGHL
Trigger
LLM
Logic
Output
Database
CRM
KEY METRICS
~190–220
Total Nodes
5 core flows
Primary Flows
3
Repeated Modular Lanes
Orchestrator + Modular Sub-Workflows
Design Pattern
Enterprise / System-Level
Complexity Level
ARCHITECTURE

High-Level Architecture

Master Orchestrator (n8n)
├── Client Intake & Asset Collection
├── AI Transcript & Data Processing
├── Document Generation Engine
├── Approval State Machine
├── Publishing & Archiving
└── Notification & Monitoring System

Workflow breakdown

PHASE 01

Phase 1: Client Intake & Asset Collection

Automated ingestion and processing of client information and assets

  • Trigger: Client call completed, transcript automatically ingested
  • AI Processing: Call summarization, entity extraction (company, industry, objectives), intent & sentiment classification
  • Asset Collection: Client URLs (website, social profiles), image assets (logos, headshots, product images), PDF assets (brochures, reports, prior publications)
  • Validation & Storage: File type and size validation, structured Google Drive folder creation, metadata indexing in Airtable
~ ~25–30
PHASE 02

Phase 2: AI-Driven Document Drafting

Intelligent document creation using AI

  • Industry-based template selection
  • Automated press release drafting
  • Smart content placement using extracted assets
  • Initial formatting and structure validation
  • Outputs: Internal working draft, status logged in Airtable
~ ~8–10
PHASE 03

Phase 3: Multi-Format Document Generation

Dynamic content injection and multi-format output

  • Dynamic Content Injection: Hyperlinked URLs, auto-resized and positioned images, extracted PDF quotes and data
  • Generated Formats: Google Docs (editable), PDF (client-ready), HTML (email compatible), social media snippets
~ Integrated across core and modular lanes
PHASE 04

Phase 4: Client Approval State Machine

Critical system component implementing state-driven approval workflow

  • Approval States: PENDING → SENT → VIEWED → APPROVED → PUBLISHED / REJECTED → REVISION / CHANGES_REQUESTED → REVISION / NO_RESPONSE → ESCALATION
  • Automated Behaviors: Approval timestamp logging, change request parsing, revision task creation
  • Follow-up reminders (Day 3 / Day 7 / Day 14)
  • Escalation to internal owner
~ ~30–35
PHASE 05

Phase 5: Final Assembly & Publishing

Quality control and publication pipeline

  • Final content merge
  • Quality control validation
  • Version locking
  • Archive to long-term storage
  • Publishing queue handoff
~ ~25–30
PHASE 06

Phase 6: Modular Processing Lanes

Three parallel sub-workflows demonstrating reusable n8n design

  • Press Release Pipeline: Template logic, asset injection, formatting, version control (~35–40 nodes)
  • Proposal Generation Pipeline: Pricing logic, terms insertion, client-specific customization (~35–40 nodes)
  • Contract & Engagement Pipeline: Legal document assembly, e-signature integration, compliance checks, secure archiving (~35–40 nodes)
~ ~105–120 combined

Technical highlights

Asset Management Engine

Assets = {
  urls: { validate: true, autoLink: true },
  images: {
    formats: ["jpg", "png", "svg"],
    maxSize: "5MB",
    processing: "resize + optimize"
  },
  pdfs: {
    extraction: "text + metadata",
    secureStorage: true
  }
}

Error Handling Strategy

  • Section-level try/catch logic
  • Centralized Airtable error logging
  • Automatic retries for transient API failures

Performance Optimization

  • Rate-limited API calls
  • Parallel execution where safe
  • Async handling for non-blocking tasks

Monitoring & Reporting

  • Airtable dashboard: Client status, approval stage, time-to-publish metrics
  • Execution success tracking
  • Manual override controls

Why this matters

01

Demonstrates advanced n8n architecture design

02

Shows large-scale automation planning capabilities

03

Implements state-driven logic at enterprise scale

04

Proves n8n can operate as a true enterprise platform

05

Replaces multiple SaaS tools with a single, maintainable automation system

WHO THIS IS FOR
01Agencies
02PR firms
03Consulting teams
04Content & publishing operations
05Client onboarding systems
NEXT NODE IN THE WORKFLOW
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