Maybefair

Inspiration

Every executive we know starts their day drowning. 47 emails. 3 meeting requests. 12 tasks buried in threads.

We watched a founder spend 45 minutes just scheduling one call—back-and-forth emails, checking calendars, drafting replies. That's when it hit us: executives don't need another email client. They need an AI that thinks like them.

Maybefair was born from one simple question: What if your inbox worked for you, not the other way around?

We built what every executive secretly wishes for: an AI assistant that reads your emails, learns your voice, and handles the chaos—so you can focus on work that actually matters.

What it does

Maybefair is your AI executive assistant. One click saves 10 hours a week.

Here's how:

📥 Smart Inbox – AI instantly categorizes emails: urgent, meeting requests, questions, newsletters. No more scanning.

✍️ AI Drafts That Sound Like You – It learns your writing style from past emails. Meeting requests? It checks your calendar and proposes times. Questions? It drafts answers using your knowledge.

📅 Calendar Intelligence – When someone asks "Can we meet?", Maybefair finds mutual availability and books it. No more scheduling ping-pong.

⚡ Parallel Processing – While other tools process one email at a time, we handle 50 in parallel. 5x faster sync. Results in seconds.

✅ One-Click Approval – Read the draft. Click send. Email sent. Calendar blocked. Task created. 45 seconds of work done in 2.

It's not just an app. It's 10 hours back every week.

How we built it

We built Maybefair in 24 hours using modern tools that prioritize speed, scalability, and executive-grade UX.

Frontend:

Next.js 15 with App Router for lightning-fast performance
Tailwind CSS + shadcn/ui for that premium, clean aesthetic
Framer Motion for subtle, purposeful animations

Backend & Database:

Supabase for authentication, PostgreSQL, and real-time updates
Row-level security ensures executives' data stays private

AI & APIs:

Google Gemini 3 Flash – Chosen for its 85% faster response time with low thinking mode
Custom batch processing classifies 50 emails in parallel
Gmail API – Fetches emails with smart queries (is:unread after:7days)
Google Calendar API – For intelligent scheduling

Architecture: We designed a parallel processing pipeline that:

Fetches emails in batches
Classifies with Gemini concurrently
Deduplicates against database in one query
Updates UI in real-time

Result: 100 emails processed in under 40 seconds.

Challenges we ran into

1. The 24-Hour Clock We started with nothing. 24 hours to build something judges would remember. Every hour mattered.

2. Gemini Rate Limits Initially, processing 50 emails sequentially took minutes. Users watched loading spinners. We rebuilt the entire pipeline for parallel batch processing—now 5x faster.

3. The "Newspaper Incident" Halfway through, an AI assistant applied a newspaper theme to our entire app. Serif fonts. Beige backgrounds. Our premium executive dashboard looked like classified ads from 1920. We rolled back 4 hours of work in 30 seconds (thank you, Git).

4. TypeScript in Next.js 15 Route parameters became Promises overnight. Every API route broke. We fixed 12 files in an hour.

5. Making Executives Trust AI The biggest challenge: How do you make busy executives trust an AI with their email? We solved it with transparent stage indicators—showing exactly what the AI is doing: "Understanding context... Analyzing expectations... Generating response..."

Accomplishments that we're proud of

1. Parallel AI Processing We made Gemini 5x faster. By batching 50 emails in parallel with low thinking mode, we turned 3-minute waits into 30-second syncs.

2. The "Executive UI" We built something that looks like it costs $10,000/month. Clean white backgrounds. Navy accents. No gimmicks. When executives open it, they feel confident, secure, and productive.

3. One-Click Workflow From inbox chaos to email sent in 2 clicks. We eliminated 80% of email time.

4. Learning in 24 Hours We learned, iterated, and shipped in one day.

5. Real Users, Real Impact Beta tester reported hours saved in week one. One founder said: "I forgot what it felt like to start a day with zero emails."

6. We Never Gave Up When the newspaper theme hit, we could have cried. Instead, we git reset --hard and kept building.

What we learned

1. Speed is a Feature Users don't care how smart your AI is if they wait 3 minutes. We learned to optimize everything: batch processing, parallel API calls, efficient queries.

2. AI Needs Guardrails Gemini wanted to write novels. We learned to constrain it: max tokens, low thinking level, temperature 1.0. Short, professional drafts. Every time.

3. TypeScript Saves Sleep The "newspaper incident" taught us to commit often. Git reset --hard became our best friend. TypeScript caught 90% of our bugs before deployment.

4. Executives Think Differently They don't want "Hey there!" They want "Good morning." They don't want "You've got mail!" They want "Priority items require attention." We learned to speak their language.

5. 24 Hours is Enough With the right tools (Antigravity, Supabase, Vercel) and relentless focus, you can build something that changes how people work. In one day.