Case Study

21 days

Solo Built

Clario Hub

You subscribe to ChatGPT, a summarizer, a writing assistant, and a note app. That’s $60 to $100/month across 4 different logins. None of them share context. You paste text between tools all day. There’s no unified AI workspace, just a pile of tabs and subscriptions draining your budget.

1. The Founder's Problem

2. Why Traditional Solutions Failed

✗Multi-tool subscriptions drain your budget with no clear ROI. You pay for overlap.
✗Context switching between apps wastes 20+ minutes daily. Every tab switch breaks flow.
✗No single platform handles summarization, chat, writing, AND notes well. They specialize into uselessness.

3. What We Built

✓5 AI workflows in one app: summarization (10 modes), chat, writing (25 combinations), meeting notes, quick notes
✓Groq AI with intelligent model routing: Llama 3.1 8B for chat, Llama 3.3 70B for content generation
✓PostgreSQL schema supporting 5 feature types with unified usage tracking across all workflows
✓Stripe tiered pricing: Free (100 requests/month) and Pro (1000) with usage-based metering

4. The Process

WEEK 1

Feature architecture (3 days), Database design (2 days), Auth + OAuth (2 days)

WEEK 2

AI integration (5 days), Usage tracking system (2 days)

WEEK 3

Stripe integration (3 days), Deploy (1 day), Polish (1 day)

5. Results & Metrics

Users

41 signups

Revenue

$0 (test mode)

GitHub

13 stars

Performance

Summaries in <2s, chat in <500ms, 99.9% uptime

Code

94.8% TypeScript, 25 commits

6. Tech Stack & Why

Next.js 16Server actions replaced Express entirely. Fewer dependencies, faster routes.

Groq Llama10x cheaper than GPT-4. 800 tokens/s vs 40 tokens/s. Not even close.

SupabaseAuth + Postgres in one SDK. Free tier scales to thousands of users.

StripeBest-in-class subscription SDK for TypeScript. Idempotency built in.

TypeScriptEnd-to-end type safety across all 5 AI modules. Caught mismatches before runtime.

Tailwind CSSUtility-first. Built all 5 feature UIs with zero custom CSS leaks.

Feature-based folders saved the project. Monolithic structure would’ve been chaos with 5 workflows.
API cost monitoring must be set up before launch, not after. First surprise bill was a wake-up call.
80% of users only touch 2 or 3 features. Build breadth later. Depth first on the core use case.

7. Lessons Learned

Feature-based folders saved the project. Monolithic structure would’ve been chaos with 5 workflows.
API cost monitoring must be set up before launch, not after. First surprise bill was a wake-up call.
80% of users only touch 2 or 3 features. Build breadth later. Depth first on the core use case.

What This Actually Proves

✓I ship fast: 7 MVPs in 14 to 21 days each. Not prototypes, actual working products with auth, payments, databases.
✓I'm honest about metrics: 10 to 50 users, $0 revenue (test mode), 7 to 10 GitHub stars. No fake numbers.
✓I know what to cut: Real-time features, team collaboration, mobile apps. Ship core value first.
✓I learn from mistakes: RLS policies are hard. Prompt engineering takes 15+ iterations. Financial calculations need edge case testing.
✓I justify tech choices: Supabase over Firebase (cheaper, better for relational data). Groq over OpenAI (10x cheaper, 800 tokens/s).

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Case Study

21 days

Solo Built

Clario Hub

1. The Founder's Problem

2. Why Traditional Solutions Failed

✗Multi-tool subscriptions drain your budget with no clear ROI. You pay for overlap.
✗Context switching between apps wastes 20+ minutes daily. Every tab switch breaks flow.
✗No single platform handles summarization, chat, writing, AND notes well. They specialize into uselessness.

3. What We Built

✓5 AI workflows in one app: summarization (10 modes), chat, writing (25 combinations), meeting notes, quick notes
✓Groq AI with intelligent model routing: Llama 3.1 8B for chat, Llama 3.3 70B for content generation
✓PostgreSQL schema supporting 5 feature types with unified usage tracking across all workflows
✓Stripe tiered pricing: Free (100 requests/month) and Pro (1000) with usage-based metering

4. The Process

WEEK 1

Feature architecture (3 days), Database design (2 days), Auth + OAuth (2 days)

WEEK 2

AI integration (5 days), Usage tracking system (2 days)

WEEK 3

Stripe integration (3 days), Deploy (1 day), Polish (1 day)

5. Results & Metrics

Users

41 signups

Revenue

$0 (test mode)

GitHub

13 stars

Performance

Summaries in <2s, chat in <500ms, 99.9% uptime

Code

94.8% TypeScript, 25 commits

6. Tech Stack & Why

Next.js 16Server actions replaced Express entirely. Fewer dependencies, faster routes.

Groq Llama10x cheaper than GPT-4. 800 tokens/s vs 40 tokens/s. Not even close.

SupabaseAuth + Postgres in one SDK. Free tier scales to thousands of users.

StripeBest-in-class subscription SDK for TypeScript. Idempotency built in.

TypeScriptEnd-to-end type safety across all 5 AI modules. Caught mismatches before runtime.

Tailwind CSSUtility-first. Built all 5 feature UIs with zero custom CSS leaks.

Feature-based folders saved the project. Monolithic structure would’ve been chaos with 5 workflows.
API cost monitoring must be set up before launch, not after. First surprise bill was a wake-up call.
80% of users only touch 2 or 3 features. Build breadth later. Depth first on the core use case.

7. Lessons Learned

Feature-based folders saved the project. Monolithic structure would’ve been chaos with 5 workflows.
API cost monitoring must be set up before launch, not after. First surprise bill was a wake-up call.
80% of users only touch 2 or 3 features. Build breadth later. Depth first on the core use case.

What This Actually Proves

✓I ship fast: 7 MVPs in 14 to 21 days each. Not prototypes, actual working products with auth, payments, databases.
✓I'm honest about metrics: 10 to 50 users, $0 revenue (test mode), 7 to 10 GitHub stars. No fake numbers.
✓I know what to cut: Real-time features, team collaboration, mobile apps. Ship core value first.
✓I learn from mistakes: RLS policies are hard. Prompt engineering takes 15+ iterations. Financial calculations need edge case testing.
✓I justify tech choices: Supabase over Firebase (cheaper, better for relational data). Groq over OpenAI (10x cheaper, 800 tokens/s).

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