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:
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.
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.ai
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