🎯 Support Intelligence Platform (ISTS)

An enterprise-grade automated customer support system that uses N8N, OpenAI, and Supabase to intelligently triage, route, and respond to customer inquiries at scale.

🚀 Project Overview

The Concept

An Intelligent Support Triage System (ISTS) that automatically:

Receives customer support requests from multiple channels (email, Slack, web forms)
Uses AI to analyze, categorize, and prioritize each ticket
Routes tickets to the right team/agent based on expertise and workload
Generates AI-assisted responses and auto-resolves simple issues
Monitors SLA compliance and provides real-time analytics

Core Features

1. Multi-Channel Intake

Email (IMAP), Slack events, HTTP webhooks
Automatic deduplication (prevents duplicate tickets)
Spam detection with AI

2. AI-Powered Analysis

Category classification (billing, technical, account, general)
Sentiment analysis (angry, frustrated, neutral, happy)
Priority scoring (urgent, high, medium, low)
Intent extraction and escalation detection

3. Smart Routing

Rule-based routing to appropriate teams
Load balancing across available agents
VIP customer escalation
Auto-resolution for simple queries (password reset, invoice requests)

4. Response Generation

AI-generated draft responses using GPT-4
Knowledge base integration for accurate answers
Human-in-the-loop approval for high-stakes responses
Auto-send for high-confidence simple issues

5. Monitoring & Analytics

SLA tracking with breach alerts
Real-time dashboard metrics
Daily performance reports
AI accuracy monitoring

🏗️ System Architecture

┌─────────────────────────────────────────────┐
│         INTAKE CHANNELS                     │
│  Email │ Slack │ Web Forms │ API Webhooks   │
└────────┬────────────────────────────────────┘
         │
         ▼
┌─────────────────────────────────────────────┐
│         N8N ORCHESTRATION LAYER             │
│  Workflow Engine (6 core workflows)         │
└────────┬────────────────────────────────────┘
         │
    ┌────┴────┐
    ▼         ▼
┌───────┐  ┌────────────┐
│OpenAI │  │  Supabase  │
│  API  │  │ (Postgres) │
└───────┘  └────────────┘
              │
              ▼
        ┌─────────────┐
        │  OUTPUTS    │
        │ Slack, Email│
        │  Webhooks   │
        └─────────────┘

Tech Stack

Component	Technology
Workflow Engine	N8N (self-hosted or cloud)
AI/ML	OpenAI GPT-4o + GPT-4o-mini
Database	Supabase (managed Postgres)
Notifications	Slack API
Email	IMAP/SMTP
Monitoring	Database logging + optional Grafana

🔄 N8N Workflow Design

6 Core Workflows

Workflow 1: Ticket Intake (12 nodes, ~2-3 hours)

Triggers:

IMAP Email Trigger (polls every 2 min)
Slack Event Trigger (#customer-support channel)
Webhook (POST /api/tickets/create)

Flow:

Trigger → Normalize Input → Generate Hash → Check Duplicate
                                              ↓
                                         [If New]
                                              ↓
                                        Spam Check (AI)
                                              ↓
                                         [If Legit]
                                              ↓
                                      Create Ticket Record
                                              ↓
                                        Log Event
                                              ↓
                                    Trigger AI Triage

Key Nodes:

Email Trigger: IMAP connection to [email protected]
Function - Normalize: Converts all channels to standard format
Function - Generate Hash: SHA-256 of content for deduplication
Postgres - Check Duplicate: Query by hash within 24h window
OpenAI - Spam Detection: gpt-4o-mini, confidence >0.9
Postgres - Create Ticket: Insert with status='new'
Postgres - Log Event: Audit trail entry

Error Handling:

Retry 3x with exponential backoff
Dead letter queue for failures
Slack alert on critical errors

Workflow 2: AI Triage (15 nodes, ~3-4 hours)

Trigger: Postgres trigger (new tickets) or N8N schedule polling

Flow:

New Ticket → Fetch Context → Build AI Context
                                  ↓
           ┌──────────────────────┼──────────────────┐
           ▼                      ▼                  ▼
    Classification          Sentiment           Priority
    (gpt-4o-mini)          (gpt-4o-mini)       (gpt-4o-mini)
           │                      │                  │
           └──────────────────────┴──────────────────┘
                                  ↓
                          Merge Results
                                  ↓
                         Store Analysis
                                  ↓
                        Update Ticket
                                  ↓
                       Trigger Routing

AI Prompts (Simplified):

Classification:

System: You are a ticket classifier. Categories: billing, technical, account, general.
Return JSON: { "category": "...", "sub_category": "...", "confidence": 0.95 }

User: Classify this ticket:
Subject: [subject]
Body: [body]
Customer History: [last 3 tickets]

Sentiment:

System: Analyze sentiment. Emotions: angry, frustrated, confused, neutral, satisfied, happy.
Intensity: 1-10 scale. Flag escalation if intensity >8 and negative.
Return JSON: { "emotion": "...", "intensity": 7, "escalation_needed": true }

User: Analyze: [ticket content]

Priority:

System: Score priority based on: business impact, customer tier, sentiment, urgency keywords.
Levels: urgent, high, medium, low
Return JSON: { "priority": "high", "urgency_score": 8, "reasoning": "..." }

User: Ticket: [content]
Sentiment: [emotion] (intensity: [X]/10)
Customer Tier: [tier]

Parallel Processing: All 3 AI calls run simultaneously, then merge results

Workflow 3: Routing & Assignment (10 nodes, ~2 hours)

Flow:

Analyzed Ticket → Check Auto-Resolve Eligibility
                           ↓
                    [If Simple + High Confidence]
                           ↓
                   Auto-Response → Send → Close

                    [If Needs Human]
                           ↓
                  Fetch Routing Rules
                           ↓
                  Check Escalation Flags
                           ↓
                  Find Available Agent
                           ↓
                    Assign Ticket
                           ↓
                  Notify via Slack
                           ↓
                   Start SLA Timer

Auto-Resolve Criteria:

canAutoResolve = (
  confidence > 0.85 &&
  sub_category in ['password_reset', 'invoice_request', 'documentation'] &&
  sentiment_intensity < 7 &&
  body.length < 500
)

Agent Selection Logic:

-- Find least-loaded agent with matching skills
SELECT a.id, a.name, COUNT(t.id) as current_load
FROM agents a
LEFT JOIN tickets t ON t.assigned_to = a.id AND t.status IN ('assigned', 'in_progress')
WHERE a.team_id = [matched_team]
  AND a.is_available = true
  AND (a.skill_level = 'senior' OR [priority] != 'urgent')
GROUP BY a.id
ORDER BY current_load ASC, RANDOM()
LIMIT 1

Slack Notification:

🎫 New Ticket Assigned: #12345

From: John Doe ([email protected])
Subject: Cannot log into account
Priority: High ⚠️
Category: account > cannot_login
Sentiment: frustrated (7/10)

AI Insights:
- Customer tried multiple password resets
- Third contact this week - needs escalation
- SLA Deadline: 4 hours

[View Ticket] [Reassign] [Escalate]

Workflow 4: Response Generation (13 nodes, ~3 hours)

Flow:

Request Response → Fetch Ticket + History → Search Knowledge Base
                                                    ↓
                                          Generate AI Response
                                          (gpt-4o, temp=0.7)
                                                    ↓
                                            Quality Check
                                                    ↓
                            ┌───────────────────────┴──────────────┐
                            ▼                                      ▼
                    [Auto-Approve]                      [Needs Review]
                            ↓                                      ↓
                    Send Immediately                      Save as Draft
                            ↓                                      ↓
                      Log Response                         Notify Agent
                            ↓
                  Schedule Follow-up (24h)

AI Response Prompt:

System: You are a customer support agent for [Company].
Personality: Friendly, empathetic, solution-oriented
Guidelines:
- Personalize with customer name
- Acknowledge their issue with empathy
- Provide clear, actionable solution
- Offer additional help
- 150-300 words

Structure:
1. Greeting
2. Acknowledgment
3. Solution/Next steps
4. Additional resources
5. Closing

User:
Customer: [name]
Ticket: [subject and body]
Sentiment: [emotion] (intensity: [X]/10)
Category: [category]
Customer History: [recent tickets]
Relevant KB: [articles]

Draft a response.

Quality Checks Before Sending:

qualityChecks = {
  has_greeting: /^(hi|hello|dear)/i.test(response),
  has_closing: /(regards|best|thanks)/i.test(response),
  length_ok: response.length >= 150 && response.length <= 1000,
  no_placeholders: !response.includes('[INSERT'),
  mentions_name: response.includes(customerFirstName),
  no_unauthorized_promises: !response.match(/will refund|guarantee/)
}

if (allPass && confidence > 0.9) {
  sendImmediately()
} else {
  saveDraftForReview()
}

Workflow 5: SLA Monitor (8 nodes, ~1 hour)

Trigger: Schedule (every 5 minutes)

Flow:

Scheduled Run → Fetch Active Tickets with SLA
                        ↓
                [For Each Ticket]
                        ↓
               Calculate Time Remaining
                        ↓
        ┌───────────────┼───────────────┐
        ▼               ▼               ▼
    50% Time      80% Elapsed     100% Breach
        ↓               ↓               ↓
    Info Alert    Warning Alert   Critical Alert
        ↓               ↓               ↓
   Slack Notify   Slack + Email   Escalate + Log

SLA Thresholds:

Urgent: 1h first response, 4h resolution
High: 4h first response, 8h resolution
Medium: 8h first response, 24h resolution
Low: 24h first response, 72h resolution

Workflow 6: Analytics & Reporting (10 nodes, ~2 hours)

Trigger: Schedule (daily at 9 AM)

Flow:

Daily Trigger → Calculate Metrics → Aggregate by Category/Priority
                                              ↓
                                    Store in analytics_daily
                                              ↓
                                      Generate Report
                                              ↓
                                     Send to Slack
                                              ↓
                              [Optional: Export to Dashboard]

Metrics Calculated:

-- Daily aggregation query
WITH daily_stats AS (
  SELECT
    DATE(created_at) as date,
    COUNT(*) as total_tickets,
    COUNT(*) FILTER (WHERE status = 'resolved') as resolved,
    AVG(EXTRACT(EPOCH FROM (first_response_at - created_at))/60) as avg_first_response_min,
    AVG(EXTRACT(EPOCH FROM (resolved_at - created_at))/3600) as avg_resolution_hours,
    COUNT(*) FILTER (WHERE sla_deadline < resolved_at) as sla_breaches,
    jsonb_object_agg(category, category_count) as by_category
  FROM tickets
  WHERE created_at >= CURRENT_DATE - INTERVAL '1 day'
  GROUP BY DATE(created_at)
)
SELECT * FROM daily_stats;

Slack Report:

📊 Daily Support Metrics - Dec 15, 2024

Total Tickets: 247
Resolved: 201 (81%)
Auto-Resolved: 95 (38%)

⏱️ Performance:
Avg First Response: 32 min
Avg Resolution: 4.2 hours

✅ SLA Compliance: 94%
❌ Breaches: 14 tickets

🔥 Top Issues:
1. billing > refund_request (43 tickets)
2. technical > bug_report (38 tickets)
3. account > password_reset (31 tickets)

🤖 AI Metrics:
Classification Accuracy: 92%
AI Responses Generated: 112
AI Responses Sent: 95 (85% approval rate)
Total AI Cost: $4.87

[View Full Dashboard →]

Error Handling & Fallbacks

// Wrapper for all OpenAI calls
async function callOpenAI(config) {
  try {
    return await openai.chat.completions.create(config)
  } catch (error) {

    // Rate limit → retry with backoff
    if (error.code === 'rate_limit_error') {
      await sleep(Math.pow(2, attempt) * 1000)
      return retry()
    }

    // Context too long → truncate
    if (error.code === 'context_length_exceeded') {
      config.messages[1].content = truncate(config.messages[1].content, 0.7)
      return retry()
    }

    // API error → use fallback
    return fallbackClassification(ticket)
  }
}

// Rule-based fallback
function fallbackClassification(ticket) {
  const text = (ticket.subject + ' ' + ticket.body).toLowerCase()

  if (text.match(/refund|charge|payment|invoice/))
    return { category: 'billing', confidence: 0.6 }
  if (text.match(/bug|error|broken|crash/))
    return { category: 'technical', confidence: 0.6 }
  if (text.match(/password|login|access/))
    return { category: 'account', confidence: 0.6 }

  return { category: 'general', confidence: 0.5 }
}

Cost Optimization

1. Caching Similar Tickets:

// Hash ticket content, check if analyzed in last 24h
const hash = sha256(ticket.subject + ticket.body.substring(0, 200))
const cached = await db.query(
  'SELECT * FROM ai_analysis WHERE content_hash = $1 AND created_at > NOW() - INTERVAL \'24 hours\'',
  [hash]
)
if (cached.rows.length > 0) return cached.rows[0] // Reuse

2. Batch Processing:

Process 10 tickets at once when load is high
Reduces API overhead

3. Smart Model Selection:

Use gpt-4o-mini for all classification tasks (60x cheaper than GPT-4)
Reserve gpt-4o only for customer-facing responses

🗄️ Database Schema

Core Tables (Postgres/Supabase)

1. tickets (Main table)

CREATE TABLE tickets (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  idempotency_key VARCHAR(64) UNIQUE NOT NULL, -- Deduplication

  -- Source
  channel VARCHAR(20) NOT NULL, -- email, slack, api, web
  sender_email VARCHAR(255) NOT NULL,
  sender_name VARCHAR(255),

  -- Content
  subject TEXT NOT NULL,
  body TEXT NOT NULL,
  attachments JSONB DEFAULT '[]',

  -- Classification
  category VARCHAR(50), -- billing, technical, account, general
  sub_category VARCHAR(50),
  tags TEXT[],

  -- Status
  status VARCHAR(30) DEFAULT 'new', -- new, analyzing, analyzed, assigned, in_progress, resolved, closed
  priority VARCHAR(20) DEFAULT 'medium', -- urgent, high, medium, low

  -- Assignment
  assigned_to UUID REFERENCES agents(id),
  assigned_at TIMESTAMP WITH TIME ZONE,
  team_id UUID REFERENCES teams(id),

  -- SLA
  sla_deadline TIMESTAMP WITH TIME ZONE,
  first_response_at TIMESTAMP WITH TIME ZONE,
  resolved_at TIMESTAMP WITH TIME ZONE,

  -- Flags
  is_spam BOOLEAN DEFAULT false,
  is_escalated BOOLEAN DEFAULT false,

  -- Metadata
  raw_metadata JSONB,

  created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
  updated_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);

-- Indexes for performance
CREATE INDEX idx_tickets_status ON tickets(status);
CREATE INDEX idx_tickets_priority ON tickets(priority);
CREATE INDEX idx_tickets_assigned_to ON tickets(assigned_to);
CREATE INDEX idx_tickets_sla_deadline ON tickets(sla_deadline) WHERE status NOT IN ('resolved', 'closed');

2. ai_analysis (AI outputs)

CREATE TABLE ai_analysis (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  ticket_id UUID REFERENCES tickets(id) ON DELETE CASCADE,

  -- Classification
  category VARCHAR(50) NOT NULL,
  sub_category VARCHAR(50),
  confidence NUMERIC(3,2) NOT NULL, -- 0.00 to 1.00

  -- Sentiment
  sentiment_emotion VARCHAR(30),
  sentiment_intensity INTEGER CHECK (sentiment_intensity BETWEEN 1 AND 10),

  -- Priority
  priority_recommendation VARCHAR(20),
  urgency_score INTEGER,
  escalation_flag BOOLEAN DEFAULT false,

  -- AI reasoning (for transparency)
  reasoning JSONB,

  -- Model info
  model_name VARCHAR(100),
  prompt_tokens INTEGER,
  completion_tokens INTEGER,
  total_cost NUMERIC(10,6),

  created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);

3. ticket_events (Audit trail)

CREATE TABLE ticket_events (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  ticket_id UUID REFERENCES tickets(id) ON DELETE CASCADE,

  event_type VARCHAR(50) NOT NULL, -- created, analyzed, assigned, status_changed, etc.
  event_data JSONB,

  actor_type VARCHAR(20), -- system, ai, agent, customer
  actor_id UUID,

  created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);

CREATE INDEX idx_ticket_events_ticket_id ON ticket_events(ticket_id, created_at DESC);

4. agents

CREATE TABLE agents (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  email VARCHAR(255) UNIQUE NOT NULL,
  name VARCHAR(255) NOT NULL,

  team_id UUID REFERENCES teams(id),
  role VARCHAR(50) DEFAULT 'agent', -- agent, senior_agent, manager
  skill_level VARCHAR(20) DEFAULT 'mid', -- junior, mid, senior, expert

  specializations TEXT[], -- ['billing', 'technical']
  max_concurrent_tickets INTEGER DEFAULT 10,

  is_available BOOLEAN DEFAULT true,
  slack_user_id VARCHAR(50),

  created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);

5. teams

CREATE TABLE teams (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  name VARCHAR(100) NOT NULL,
  handles_categories TEXT[], -- ['billing', 'technical']
  slack_channel_id VARCHAR(50),
  created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);

-- Seed data
INSERT INTO teams (name, handles_categories, slack_channel_id) VALUES
  ('Billing Team', ARRAY['billing'], 'C01234ABC'),
  ('Technical Support', ARRAY['technical'], 'C01234DEF'),
  ('Account Management', ARRAY['account'], 'C01234GHI'),
  ('General Support', ARRAY['general'], 'C01234JKL');

6. routing_rules

CREATE TABLE routing_rules (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  name VARCHAR(100) NOT NULL,

  -- Match criteria
  category VARCHAR(50),
  priority VARCHAR(20),

  -- Target
  team_id UUID REFERENCES teams(id),

  rule_priority INTEGER DEFAULT 100, -- Lower = higher priority
  is_active BOOLEAN DEFAULT true,

  created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);

7. sla_tracking

CREATE TABLE sla_tracking (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  ticket_id UUID REFERENCES tickets(id) ON DELETE CASCADE,

  priority VARCHAR(20) NOT NULL,
  first_response_deadline TIMESTAMP WITH TIME ZONE NOT NULL,
  resolution_deadline TIMESTAMP WITH TIME ZONE NOT NULL,

  status VARCHAR(20) DEFAULT 'active', -- active, met, breached

  alert_50_sent BOOLEAN DEFAULT false,
  alert_80_sent BOOLEAN DEFAULT false,
  breach_alert_sent BOOLEAN DEFAULT false,

  created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);

8. responses

CREATE TABLE responses (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  ticket_id UUID REFERENCES tickets(id) ON DELETE CASCADE,

  direction VARCHAR(20) NOT NULL, -- outbound, inbound
  channel VARCHAR(20) NOT NULL,
  body TEXT NOT NULL,

  sender_type VARCHAR(20), -- agent, customer, ai
  sender_id UUID,

  was_ai_generated BOOLEAN DEFAULT false,
  ai_draft BOOLEAN DEFAULT false, -- Saved for review vs sent

  sent_at TIMESTAMP WITH TIME ZONE,
  created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);

9. error_logs

CREATE TABLE error_logs (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),

  workflow_name VARCHAR(100),
  node_name VARCHAR(100),
  error_type VARCHAR(100),
  error_message TEXT,

  ticket_id UUID REFERENCES tickets(id),
  severity VARCHAR(20) DEFAULT 'error',

  resolved BOOLEAN DEFAULT false,
  created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);

10. analytics_daily

CREATE TABLE analytics_daily (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  date DATE UNIQUE NOT NULL,

  total_tickets INTEGER,
  tickets_resolved INTEGER,
  tickets_auto_resolved INTEGER,

  avg_first_response_minutes NUMERIC(10,2),
  avg_resolution_hours NUMERIC(10,2),

  sla_compliance_rate NUMERIC(5,2),
  sla_breaches INTEGER,

  tickets_by_category JSONB,
  tickets_by_priority JSONB,

  ai_classification_accuracy NUMERIC(5,2),
  total_ai_cost NUMERIC(10,4),

  created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);

Useful Views

-- Active tickets with AI analysis
CREATE VIEW active_tickets_view AS
SELECT
  t.*,
  a.category as ai_category,
  a.confidence,
  a.sentiment_emotion,
  ag.name as agent_name
FROM tickets t
LEFT JOIN ai_analysis a ON a.ticket_id = t.id
LEFT JOIN agents ag ON ag.id = t.assigned_to
WHERE t.status NOT IN ('resolved', 'closed')
ORDER BY t.priority, t.created_at;

-- Agent workload
CREATE VIEW agent_workload AS
SELECT
  a.id,
  a.name,
  COUNT(t.id) as current_tickets,
  a.max_concurrent_tickets - COUNT(t.id) as capacity_remaining
FROM agents a
LEFT JOIN tickets t ON t.assigned_to = a.id AND t.status IN ('assigned', 'in_progress')
GROUP BY a.id
ORDER BY current_tickets DESC;

🚀 upcoming Stretch Goals

Level 1: Enhanced Features (2-4 hours each)

1. Multi-Language Support

Detect customer language with OpenAI
Auto-translate to English for processing
Respond in customer's original language
Store translations in database

2. Knowledge Base with RAG

Create KB articles table with embeddings
Use OpenAI embeddings (text-embedding-3-small)
Implement vector similarity search (pgvector)
Inject relevant KB articles into AI context

3. Customer Portal

Simple Next.js/React frontend
Submit tickets via web form
View ticket status and history
Webhook to N8N on submission

4. Advanced Analytics Dashboard

Set up Metabase or Grafana
Connect to Supabase database
Create charts: tickets over time, category breakdown, SLA compliance
Agent performance leaderboard

5. Sentiment Trend Analysis

Track sentiment changes over time
Alert if negative sentiment spike detected
Identify products/features causing issues
Proactive reach-out to frustrated customers

Level 2: Production Features (4-6 hours each)

6. A/B Testing Framework

Test different AI prompts
Track which performs better
Automatically promote winning variant
Store test results in database

7. Agent Performance Metrics

Track: avg resolution time, customer satisfaction, tickets handled
Leaderboard in Slack
Coaching recommendations based on data

8. Smart Ticket Merging

Detect duplicate/related tickets from same customer
Suggest merging to agent
Link related tickets automatically

9. Proactive Outreach

Detect customers with multiple unresolved issues
Auto-escalate to account manager
Generate personalized "check-in" email

10. Voice/Phone Integration

Integrate Twilio for phone support
Transcribe calls with Whisper API
Create tickets from phone calls
Same AI triage process

Level 3: Enterprise Features (1-2 days each)

11. Multi-Tenant Architecture

Support multiple companies in one system
Row-level security in Supabase
Separate Slack workspaces per tenant
White-label customization

12. Custom ML Model

Fine-tune GPT-4 on your historical tickets
Compare performance vs base model
Document cost/accuracy tradeoffs

13. Compliance & Security

PII detection and redaction
GDPR compliance (data export, deletion)
SOC 2 audit logging
Encryption at rest

Quick Start Checklist

Immediate Next Steps:

# 1. Clone or create project directory
mkdir intelligent-support-system && cd intelligent-support-system

# 2. Set up Supabase (or local Postgres)
# → Go to supabase.com, create free project

# 3. Set up N8N
# Option A: Cloud (easiest)
# → Go to n8n.cloud, create account

# Option B: Docker (local)
docker run -it --rm \
  --name n8n \
  -p 5678:5678 \
  -v ~/.n8n:/home/node/.n8n \
  n8nio/n8n

# 4. Get OpenAI API key
# → Go to platform.openai.com, create API key

Copy Database Schema:
- Copy all CREATE TABLE statements from "Database Schema" section
- Run in Supabase SQL Editor
- Verify tables created successfully
Start with Workflow 1:
- Open N8N
- Create new workflow "01-ticket-intake"
- Follow build plan step-by-step
- Test each node as you add it
Test Early, Test Often:
- Don't wait until everything is built
- Test each workflow independently
- Use sample data provided

📞 Support & Questions

If you encounter issues:

Check N8N documentation: https://docs.n8n.io
OpenAI API docs: https://platform.openai.com/docs
Supabase docs: https://supabase.com/docs

Common issues:

OpenAI rate limits: Add retry logic, use exponential backoff
N8N workflow timeout: Increase timeout in settings, optimize queries
Postgres connection fails: Check firewall, verify credentials

.

Name		Name	Last commit message	Last commit date
Latest commit History 19 Commits
database		database
docs		docs
frontend		frontend
scripts		scripts
.env.example		.env.example
.gitignore		.gitignore
INSTALL.sh		INSTALL.sh
PROJECT_SUMMARY.txt		PROJECT_SUMMARY.txt
QUICKSTART.md		QUICKSTART.md
README.md		README.md

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🎯 Support Intelligence Platform (ISTS)

🚀 Project Overview

The Concept

Core Features

🏗️ System Architecture

Tech Stack

🔄 N8N Workflow Design

6 Core Workflows

Workflow 1: Ticket Intake (12 nodes, ~2-3 hours)

Workflow 2: AI Triage (15 nodes, ~3-4 hours)

Workflow 3: Routing & Assignment (10 nodes, ~2 hours)

Workflow 4: Response Generation (13 nodes, ~3 hours)

Workflow 5: SLA Monitor (8 nodes, ~1 hour)

Workflow 6: Analytics & Reporting (10 nodes, ~2 hours)

Error Handling & Fallbacks

Cost Optimization

🗄️ Database Schema

Core Tables (Postgres/Supabase)

1. tickets (Main table)

2. ai_analysis (AI outputs)

3. ticket_events (Audit trail)

4. agents

5. teams

6. routing_rules

7. sla_tracking

8. responses

9. error_logs

10. analytics_daily

Useful Views

🚀 upcoming Stretch Goals

Level 1: Enhanced Features (2-4 hours each)

Level 2: Production Features (4-6 hours each)

Level 3: Enterprise Features (1-2 days each)

Quick Start Checklist

📞 Support & Questions

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