Issue #3: How We Replaced a Paid Ticketing System in 48 Hours Using Vibe Coding

TL;DR

We replaced a paid customer support system we’d used for two years… in 48 hours.
Built 80–90% through Vibe Coding, refined manually at the end.
The result: a faster, more customized, more integrated support platform — with lower cost.
Below is the exact step-by-step process we followed so you can replicate it.
Next week: a practical guide on which AI model to use for which task and why.

1.0 Why We Did This

Like many businesses, we were paying for a solid SaaS support desk, but it had limits:

The UI didn’t match our workflow.
Customization was slow or impossible.
We were paying monthly for features we didn’t use.
Integration with our stack required workarounds.

With Vibe Coding maturing rapidly, we asked a simple question:

“Could we build exactly what we want, faster and cheaper, using conversational AI development?”

The answer: Yes. In two days.

2.0 The Build Process (Step-By-Step)

This is exactly how we built KuwareDesk, our internal support system, using Vibe Coding workflows.

Tools/Platforms used:

LLMs: ChatGPT, Claude

IDE: Cursor

Framework: Django/React

Deployment: Digital Ocean droplets

Version control: GIT

Developer Machines Setup: Windows Subsystem for Linux (WSL), Ubuntu, and Docker

2.1 Defined the Goal & Core Features

We clarified the intention: build a simple but complete support desk.
Core features included:

Ticket creation
Ticket status updates
Agent view + internal notes
User view
Dashboard + metrics

This clarity is critical; AI tools work best when the desired outcome is unambiguous.

2.2 Captured Requirements Using AI

We opened Claude and documented the system as if we were writing a product spec:

Explained the whole system
Provided examples of other ticketing tools
Broke down features one by one
Asked Claude to produce a requirements.md file

This became our “source of truth.”

2.3 Used ChatGPT to Clarify Confusing Flows

Whenever we weren’t sure how a feature should behave:

We opened a clean ChatGPT project
Asked it to explain the best workflow
Requested corrections to missing or unclear flows
Asked it to draft notification emails and edge-case rules

This created functional clarity before writing a line of code.

2.4 Built Each Feature One Workflow at a Time

Instead of asking AI to “build the entire app,” we:

Attached requirements
Gave a single feature
Asked for detailed behavior
Had AI update documentation

Vibe Coding works best feature-by-feature, not “big-bang style.”

2.5 Used Git from the Start

We initialized a repo immediately. Tracked progress with:

Daily commits
Daily conversation logs
Daily prompts for context

This ensured reproducibility and consistency.

2.6 Used Claude CLI for Vibe Coding Generation

This was the magic step. We:

Fed in requirements.md
Fed in daily notes
Fed in UI screenshots
Let the Claude CLI generate/refine real application code

This produced 80–90% of the full application.

2.7 Added Real UI Reference Screenshots

We gave AI concrete visual targets.
As a result, UI components came out structured and logically grouped.

Visual context dramatically improves AI-generated code.

2.8 Polished the Frontend Manually

AI got us most of the way there, but not all the way. We improved:

Colors
Spacing
Component alignment
UX consistency

AI built it. A human refined it.

2.9 Iterated & Tested Until It Was Solid

We:

Ran real tickets
Updated statuses
Checked analytics
Fixed issues manually or with AI help
Logged every change

By the end of Day 2, we had a full production system, replacing a SaaS tool used for two years.

3.0 Results

Went from concept → production in 48 hours
Saved monthly SaaS fees
Gained a faster, cleaner UI
Full customization freedom
Better long-term maintainability
Deeper team understanding of Vibe Coding workflows

This is the real power of AI-assisted app development.

4.0 Why This Matters for Business Leaders

Custom internal tools are no longer a luxury. With AI development workflows like Vibe Coding, any business can now:

Replace expensive SaaS tools
Build exactly what their team needs
Iterate faster than commercial vendors
Develop internal capability, not just dependencies

This is a strategic capability shift.

Businesses with AI development literacy will out-innovate those who rely solely on bought software.

5.0 Your 30-Day Play

Pick one internal tool you’re currently paying for that:

is too slow
is too complex
doesn’t match how your team works
doesn’t integrate well
costs you too much

Then:

Document the workflow with AI
Build a prototype using Vibe Coding
Test it with real users
Decide: replace, integrate, or improve

This one exercise can eliminate a cost and create an internal capability.

6.0 Signal Metric of the Week

Build-to-Deploy Time

Time from defined idea → production use

Target: Under 7 days for internal tools using Vibe Coding.

This metric measures agility, not size. It will be a competitive differentiator in 2025 and beyond.

7.0 Coming Next Week

Which Model Should You Use for Which Task and Why?

We’ll break down:

when to use GPT vs Claude vs Gemini vs Llama
which models are best for reasoning, coding, analysis, content, or vision
cost vs accuracy tradeoffs
real examples from our own work

Expect a practical, model-by-model field guide for business leaders.

8.0 Your Turn

What internal tool do you wish you could build or replace? Reply to this email, and we will choose

Thanks for reading Signal > Noise, where we separate real business signal from the AI hype.

See you next Tuesday,

Avi Kumar

Founder: Kuware.com

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