Total Processed
—
tickets this run
Draft Ready
—
awaiting CS approval
Knowledge Gaps
—
need escalation
KB Additions
—
new entries learned
Auto-answer Rate
—
of open tickets
Tickets by Buyer Persona
Tickets by Theme
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Draft Replies — review and send via the appropriate channel
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Knowledge Gaps — resolve to grow the KB automatically
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KB Additions — approve pending entries before they are added to the live Knowledge Base
🛡️
Human-in-the-Loop
Every KB entry drafted by the AI sits here as pending until a human reviews and approves it. No AI-generated content enters the live knowledge base without explicit sign-off. This ensures the workflow stays accurate, on-brand, and accountable — the AI proposes, the human decides.
Draft replies follow the same principle — AI drafts, your CS team reviews and sends. No automated customer-facing messages without a human checkpoint.
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External Sentiment Benchmarking
Reddit r/MicrobrandWatches
WatchUSeek Forums
Amazon RZE Reviews
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Morning Wu
AI Builder & Educator · Helping 100K builders learn the practical use of AI
🏆 About This Project
This AI workflow system was built for the e27 Echelon Singapore AI Workflow Competition 2026, using a real brief from Boldr — a Singapore-based watch micro-brand.
The challenge was to build a practical AI system that could meaningfully help a real business — and Boldr's brief around customer support and knowledge gaps was the perfect canvas to put those skills to work.
"A huge thank you to e27 for organizing this amazing challenge, to the sponsors for providing such valuable tools, and to Boldr for giving builders a practical, real-world problem to solve. I've enjoyed this challenge tremendously and am very much looking forward to future events."
👤 About Me
I'm building AfterWork Startup — an online AI learning platform for non-techies who want to acquire practical AI skills and build online side hustles with them. We currently serve 20,000+ users across Hong Kong and Taiwan, and are planning to expand across Southeast Asia this year.
My mission is to help 1 million non-techies master practical AI skills — turning curiosity into real, income-generating projects.
🌐 app.afterworkstartup.com →🛠 Stack Used in This Project
n8n
Workflow automation
Supabase
Database & API
Qwen AI
LLM intelligence
Tavily
Real-time web search
🧪 Add Sample Data
Insert a new ticket into the inbox, then run WF1 to see the full AI pipeline process it live. Use the quick-fill buttons below to load a realistic sample question.
Quick-fill a sample question
Ticket details
Auto-generated if left blank
What happens next:
1. Ticket is inserted into the
2. Go to n8n Cloud → WF1 → click Execute Workflow
3. Watch the Draft Replies or Knowledge Gaps tab update with the result
1. Ticket is inserted into the
tickets_inbox table in Supabase2. Go to n8n Cloud → WF1 → click Execute Workflow
3. Watch the Draft Replies or Knowledge Gaps tab update with the result
Marketing Intel Brief
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The Problem
Most companies still handle CS manually. That's costing them more than they realise.
⏳
Inefficient CS Workflow
~25 hrs
wasted per month handling just 100 tickets manually — that's nearly a full work week.
Slow replies cost sales. And because every agent writes differently, there's no consistent brand voice — quality depends on whoever's on shift.
🔁
Questions, Answered from Scratch
65%
of CS tickets are repeat questions — answered from scratch every single time, with zero learning.
The same questions come in every day — and your team answers each one from scratch. It drains time, kills motivation, and nothing gets smarter.
📡
Blind to Valuable Marketing Signals
91%
of unhappy customers leave without saying why — and the signals that could've saved them vanish with them.
Customer questions are your richest source of product and marketing intelligence. Manual CS answers and moves on — that signal never reaches the people who need it.
Your CS team is buried in repetitive tickets — and the insights they're sitting on never reach your business.
Workflow Walkthrough — How This Fixes It
Three capabilities working together to make your CS operation faster, smarter, and more valuable
🤖
AI-Powered CS Handling
Speed & consistency at scale. The workflow classifies every ticket, searches the KB, and drafts a brand-voiced reply automatically. Your CS team reviews and sends — not writes from scratch. When a gap is found, it logs it and learns from it so the same question never stumps the system twice.
Faster replies · Consistent voice · Self-improving KB
📊
Insight Engine for Marketing & Product
Turn CS noise into business signal. Weekly briefs surface top themes, buyer personas, and recurring pain points. The AI scores each gap as a marketing opportunity and cross-checks it against Reddit, WatchUSeek, and Amazon — so you know what's brand-specific vs. a genuine market need.
Weekly briefs · Scored opportunities · Market validation
⚙️
Built to Scale & Redeploy
Handles volume without adding headcount. Built on n8n + Supabase + Qwen AI — every layer is swappable. Whether you need a different AI model, database, or channel, the architecture adapts. Want to run this for your own brand? Drop your details in the Deploy This tab and I'll walk you through it.
Scalable volume · Swappable stack · Ready to deploy
01
How It Works
Three automated n8n workflows running in sequence — no human in the loop
This automation is powered by 3 workflows (built using n8n)
WF1
Core Intelligence Loop
Classifies tickets, searches KB, drafts replies or logs knowledge gaps
WF2
Self-Improving KB Loop
Resolves gaps, drafts KB entries, strengthens the knowledge base over time
WF3
Marketing Insight Generator
Aggregates gap patterns every 6 hrs, scores opportunities, generates briefs
Built with 4 tools
n8n
Workflow Architecture
The automation backbone connecting all tools
Qwen AI
The Brain
Classification, KB search, reply drafting & insight generation
Supabase
Database & Storage
Swappable — any PostgreSQL or cloud DB works depending on data scale
Tavily
Web Search
Real-time sentiment from Reddit, WatchUSeek & Amazon
End-to-End Flow
How a ticket moves through the system — from inbox to insight
📥 Ticket Submitted
In this demo, tickets are loaded from a pre-uploaded CSV. In production, this can connect to any CS channel — WhatsApp, email, live chat, and more. Contact morning@afterworkstartup.com to discuss your setup.
In this demo, tickets are loaded from a pre-uploaded CSV. In production, this can connect to any CS channel — WhatsApp, email, live chat, and more. Contact morning@afterworkstartup.com to discuss your setup.
↓
⚙️ WF1 — Core Intelligence Loop
Classify → Search KB → Check confidence
Classify → Search KB → Check confidence
↓
✓ High Confidence
✉️ Draft Reply
AI-drafted, brand-voiced
AI-drafted, brand-voiced
↓
💾 Saved to Dashboard
tickets_processed
tickets_processed
✗ Low Confidence
⚠️ Knowledge Gap Logged
+ Sentiment search via Tavily
+ Sentiment search via Tavily
↓
🔄 WF2 — Resolve Gap
CS answers → Qwen drafts KB entry
CS answers → Qwen drafts KB entry
↓
📚 KB Addition Created
Pending human approval
Pending human approval
↓
📊 WF3 — Marketing Insight Generator
Aggregate gaps → Score opportunities → Generate brief
Aggregate gaps → Score opportunities → Generate brief
WF1
Core Intelligence Loop
Trigger: Manual / Scheduled
- Load unprocessed tickets from
tickets_inbox - Build knowledge base from
knowledge_sources(FAQ, product ref, rate cards) - Classify each ticket by theme and buyer persona using Qwen AI
- Search KB for a relevant answer — evaluate confidence score
- If confident → draft a branded reply → save to
tickets_processed - If not confident → log as knowledge gap → extract gap labels with Qwen
- Search Reddit, WatchUSeek, and Amazon via Tavily for community sentiment on each gap
- Save sentiment data to
gap_sentiment— marks ticket as processed
Output: Draft replies + knowledge gaps + sentiment data
WF2
Self-Improving KB Loop
Trigger: Dashboard "Resolve" button → Webhook
- Dashboard sends gap ID + CS agent's answer to the WF2 webhook
- Qwen AI formats the answer into a clean KB entry (concise, brand-voiced)
- Qwen also drafts a customer-facing reply personalised to the original customer
- Save KB entry as pending in
kb_additions - Mark the original knowledge gap as resolved
- Return the draft reply to the dashboard instantly
- Human approves or rejects KB entry — approved entries strengthen future responses
Output: Approved KB entries + resolved gaps + draft replies
WF3
Marketing Insight Generator
Trigger: Schedule every 6 hours (when 5+ open gaps exist)
- Check if 5 or more unresolved knowledge gaps exist — skip if not enough data
- Aggregate gap patterns: themes, buyer personas, channels, recurring questions
- Qwen AI analyses patterns and scores each gap as a marketing opportunity
- Generate a full marketing brief: top themes, content recommendations, product feedback
- Score each gap for uniqueness, conversion potential, and overall opportunity
- Save brief to
marketing_briefsand per-gap scores tomarketing_insights
Output: Marketing brief + per-gap opportunity scores
📡 Supported CS Channels
This workflow is channel-agnostic — any platform that can send a webhook or connect via n8n can plug in as a ticket source. Below are the channels it supports out of the box or with minimal configuration.
Email
Gmail / Outlook via n8n trigger
Ready
WhatsApp
Via 360dialog or Twilio node
Ready
Telegram
Native n8n Telegram trigger
Ready
Web Form
Webhook from any contact form
Ready
Facebook
Page comments & Messenger DMs
Coming Soon
Instagram
DMs & comment threads
Coming Soon
TikTok
Comment monitoring & replies
Coming Soon
Custom Channel
Any platform with a webhook or API
On Request
🚀
Deploy for Your Business
This workflow is built to be redeployed — leave your details and I'll send you a full walkthrough
Who Is This For?
E-commerce & DTC Brands
High ticket volume, repetitive questions on shipping, returns, and product specs. Perfect fit for auto-drafting at scale.
Micro-brands & Niche Retailers
Small CS teams, passionate customer base. The self-improving KB means the system gets smarter with every interaction.
SMEs with CS Teams of 1–5
No budget for enterprise helpdesk tools. This runs on free-tier infrastructure and can be self-hosted on a $5/month VPS.
Builders & AI Workflow Enthusiasts
Full source code on GitHub. Fork it, adapt the knowledge base, swap in your own LLM. The architecture is modular by design.
Get the Workflow
Leave your info and let's see how I can help you build a similar workflow for your business.
💰
Cost Calculator & Scalability
How much could you save by switching to an AI-powered CS workflow? Adjust the inputs below to your team's setup and find out.
💰 ROI Calculator
Based on 80% auto-draft rate — the actual rate observed in this demo
Tickets per month
300
Avg. time per ticket (manual)
10 min
CS hourly rate
SGD 15/hr
Number of CS staff
2
🧑 Manual CS
Tickets handled—
CS staff—
Time spent—
Staff monthly salary—
Total cost—
🤖 Boldr AI Workflow
Auto-drafted (80%)—
Human review needed—
Time spent (review only)—
Workflow cost
—
AI token cost
—
Total cost—
How this is calculated:
Manual: (tickets × avg time × hourly rate) + (staff count × monthly salary)
AI Workflow: only 20% gap tickets need human review + workflow infrastructure cost
Workflow cost = $0 up to 300 tickets/month · scales $20–$100/month beyond that
Manual: (tickets × avg time × hourly rate) + (staff count × monthly salary)
AI Workflow: only 20% gap tickets need human review + workflow infrastructure cost
Workflow cost = $0 up to 300 tickets/month · scales $20–$100/month beyond that
Scaling Cost Breakdown
What does it actually cost to scale this workflow?
The total infrastructure bill for a 3+ person CS team typically runs US$10–$140/month — less than a week of one agent's salary. The stack has four components, each independently swappable, each priced on usage — not headcount.
What You're Actually Paying For
Workflow Engine
US$0 – $60 / month
The workflow logic itself is free and open-source. You only pay for hosting — n8n Cloud starts free, scales up to ~$60/month. Or self-host on a VPS for less. Cost scales with execution volume, not seat count.
AI Brain
US$10 – $50 / month
Used for ticket classification, KB search, reply drafting, and insight generation. Pay-per-token — starts at ~US$10/month for low volume, scales to ~$50/month for a growing SME workload. Swap in any LLM without touching the workflow.
Database
US$0 – $25 / month
Stores tickets, KB entries, gap logs, and briefs. Supabase free tier handles low volume — upgrade to Pro (~US$25/month) as you scale. Fully swappable: any PostgreSQL-compatible DB works without changing the schema.
Web Search
US$0 – $5 / month
Powers the external market validation layer — searches Reddit, WatchUSeek, and Amazon to benchmark your knowledge gaps against real buyer sentiment. Free tier available, paid plan from ~US$5/month for API access.
💡 Total infra cost for a 3-person CS team handling 1,000–3,000 tickets/month: roughly US$10–$140/month. Every component is swappable — tighter budget means free-tier tools, higher volume means a single plan upgrade per service. No re-architecture needed.
Side-by-Side Comparison
AI Workflow vs. Manual CS vs. Generic Chatbot
See exactly where this workflow pulls ahead — and where the others fall short.
| Capability | Manual CS | Generic Chatbot | Boldr AI Workflow |
|---|---|---|---|
| Response drafting | Manual — 15–30 min/ticket | Instant but generic | ✓ AI-drafted, brand-voiced |
| Knowledge gap detection | Never detected | Ignored or escalated | ✓ Auto-logged + labelled |
| KB self-improvement | Manager updates manually | Static — no learning | ✓ Gets smarter after each gap |
| Marketing insights | Quarterly manual reports | None | ✓ Auto-generated weekly |
| External market signals | None | None | ✓ Reddit + forums + Amazon |
| Monthly infrastructure cost | Staff salary only | $200–$500/month | ✓ ~$10–$140/month total |
| Scales with ticket volume | Hire more headcount | Limited by plan tier | ✓ Single plan upgrade per tool |
| Self-hostable / open stack | N/A | Usually locked-in | ✓ Docker + n8n + any LLM |