Case Study

17 days

Solo Built

Loopr

You’re a solo founder running outbound. Your leads are spread across a spreadsheet, your inbox, and sticky notes. Enterprise CRMs overwhelm you with features designed for sales teams of 20. You don’t need a pipeline manager, you need someone to tell you who to email today and why.

1. The Founder's Problem

2. Why Traditional Solutions Failed

✗Traditional CRMs are designed for enterprise sales teams. Solo founders drown in unused features.
✗Lead context lives everywhere: sheets, notes, emails. Finding a single conversation history takes 5 minutes.
✗No tool combines CRM tracking with daily AI guidance. You get notifications, not direction.
✗Most products add gamification and noise. You need fewer pings, not more.

3. What We Built

✓Dual pipeline views: spreadsheet mode for fast edits, Kanban mode for stage-based movement
✓AI daily briefing that tells you exactly who to follow up with and why, every morning
✓Signal Score: each lead rated 0 to 100 based on reply patterns, engagement, and timing
✓Reply analyzer that detects sentiment and suggests next steps from message content
✓Single-tenant data model with Supabase RLS and CSV export for backup
✓Pipeline analytics: stage conversion rates, velocity trends, and performance charts
✓Calm, focused interface. No notification badges, no streaks, no noise.

4. The Process

WEEK 1

Pipeline schema + auth + RLS foundation (4 days), sheet/kanban UI architecture (3 days)

WEEK 2

AI briefing + reply analyzer integration (4 days), signal scoring logic + lead workflows (3 days)

WEEK 3

Analytics layer + polish + deployment hardening (3 days)

5. Results & Metrics

Users

31 signups

Revenue

$0 (early beta)

GitHub

10 stars

Performance

Pipeline interactions in <1 second, analytics updates in real-time

Code

TypeScript-first React 19 + Vite build

6. Tech Stack & Why

React 19 + TypeScriptStrong typing caught state management bugs before they reached users. CRM state is complex.

Vite + Tailwind CSS v4Sub-second hot reload. Built the neobrutal design system in days.

TanStack QueryAutomatic cache invalidation, request dedup, and background refetching for analytics.

SupabaseAuth, Postgres, Edge Functions, and RLS in one SDK. Single-tenant isolation built in.

Groq APIConfigurable AI layer for briefings and reply analysis. Llama 3.3 70B at 800 tokens/second.

Solo founders value clarity over features. Removing noise increased daily active usage.
AI briefings only work when tied to specific next actions. ‘Here’s what’s happening’ isn’t enough.
Signal scoring must be transparent. Users don’t trust a black-box number telling them who to call.
Dual views (sheet + kanban) served both speed editors and strategic thinkers. One view alienated half the users.

7. Lessons Learned

Solo founders value clarity over features. Removing noise increased daily active usage.
AI briefings only work when tied to specific next actions. ‘Here’s what’s happening’ isn’t enough.
Signal scoring must be transparent. Users don’t trust a black-box number telling them who to call.
Dual views (sheet + kanban) served both speed editors and strategic thinkers. One view alienated half the users.

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

17 days

Solo Built

Loopr

1. The Founder's Problem

2. Why Traditional Solutions Failed

✗Traditional CRMs are designed for enterprise sales teams. Solo founders drown in unused features.
✗Lead context lives everywhere: sheets, notes, emails. Finding a single conversation history takes 5 minutes.
✗No tool combines CRM tracking with daily AI guidance. You get notifications, not direction.
✗Most products add gamification and noise. You need fewer pings, not more.

3. What We Built

✓Dual pipeline views: spreadsheet mode for fast edits, Kanban mode for stage-based movement
✓AI daily briefing that tells you exactly who to follow up with and why, every morning
✓Signal Score: each lead rated 0 to 100 based on reply patterns, engagement, and timing
✓Reply analyzer that detects sentiment and suggests next steps from message content
✓Single-tenant data model with Supabase RLS and CSV export for backup
✓Pipeline analytics: stage conversion rates, velocity trends, and performance charts
✓Calm, focused interface. No notification badges, no streaks, no noise.

4. The Process

WEEK 1

Pipeline schema + auth + RLS foundation (4 days), sheet/kanban UI architecture (3 days)

WEEK 2

AI briefing + reply analyzer integration (4 days), signal scoring logic + lead workflows (3 days)

WEEK 3

Analytics layer + polish + deployment hardening (3 days)

5. Results & Metrics

Users

31 signups

Revenue

$0 (early beta)

GitHub

10 stars

Performance

Pipeline interactions in <1 second, analytics updates in real-time

Code

TypeScript-first React 19 + Vite build

6. Tech Stack & Why

React 19 + TypeScriptStrong typing caught state management bugs before they reached users. CRM state is complex.

Vite + Tailwind CSS v4Sub-second hot reload. Built the neobrutal design system in days.

TanStack QueryAutomatic cache invalidation, request dedup, and background refetching for analytics.

SupabaseAuth, Postgres, Edge Functions, and RLS in one SDK. Single-tenant isolation built in.

Groq APIConfigurable AI layer for briefings and reply analysis. Llama 3.3 70B at 800 tokens/second.

Solo founders value clarity over features. Removing noise increased daily active usage.
AI briefings only work when tied to specific next actions. ‘Here’s what’s happening’ isn’t enough.
Signal scoring must be transparent. Users don’t trust a black-box number telling them who to call.
Dual views (sheet + kanban) served both speed editors and strategic thinkers. One view alienated half the users.

7. Lessons Learned

Solo founders value clarity over features. Removing noise increased daily active usage.
AI briefings only work when tied to specific next actions. ‘Here’s what’s happening’ isn’t enough.
Signal scoring must be transparent. Users don’t trust a black-box number telling them who to call.
Dual views (sheet + kanban) served both speed editors and strategic thinkers. One view alienated half the users.

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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