Todobo App

Capture thoughts. Turn them into action.

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Todobo

Inspiration

Most to-do apps expect you to stop what you're doing, open the app, think about where a task belongs, choose a date, and type everything manually.

That process is surprisingly slow, especially when you're busy or an idea comes to you for only a few seconds.

I wanted task capture to feel as natural as talking to another person.

The idea behind Todobo was simple:

If you can say it, you should be able to save it.

Instead of manually filling multiple fields, users simply speak naturally:

"Remind me to pay the electricity bill next Friday."

Todobo understands the request, extracts the task, identifies the reminder date, and creates a structured to-do in seconds.

The goal is to remove friction from task management so people never lose an idea because capturing it felt like work.

What it does

Todobo is an AI-powered voice to-do app that converts natural speech into organized tasks.

Users can:

  • Record a short voice note
  • Automatically transcribe speech
  • Extract actionable tasks using AI
  • Detect dates and reminders from natural language
  • Review and edit generated tasks
  • Receive reminders at the appropriate time

Instead of requiring users to remember commands or specific formats, Todobo understands the way people naturally speak.

How I built it

Todobo consists of a native iOS application backed by an AI-powered backend.

The app records short voice notes, which are securely processed by the backend. AI models analyze the transcript, identify actionable items, understand dates and relative time expressions, and generate structured tasks.

For monetization, I integrated RevenueCat to provide a seamless subscription experience across Monthly, Yearly, and Lifetime plans while keeping purchase management simple.

The project combines modern mobile development, AI-powered natural language understanding, backend APIs, and cloud infrastructure into a fast and intuitive experience.

Challenges I ran into

Natural language is surprisingly difficult.

People rarely say things like:

"Create a task due on July 18."

Instead they say things like:

  • "Next Friday"
  • "Tomorrow evening"
  • "After my meeting"
  • "When I reach home"

Making AI consistently interpret these expressions correctly required multiple iterations and prompt improvements.

Another challenge was keeping the experience extremely fast. Capturing a task should feel nearly instant, so reducing latency throughout the pipeline became an important part of the project.

Finally, building a subscription experience that feels effortless while supporting multiple purchase options was made much easier using RevenueCat.

Accomplishments that I'm proud of

  • Building a task capture experience that feels conversational instead of form-based.
  • Creating an AI workflow that turns messy spoken language into structured tasks.
  • Shipping a polished iOS application with integrated subscriptions.
  • Successfully integrating RevenueCat for purchase management.
  • Building a product that encourages users to capture ideas immediately instead of postponing them.

What I learned

This project reinforced that great AI products aren't just about using large language models.

The real challenge is designing an experience where AI quietly removes friction without getting in the user's way.

I also gained valuable experience integrating AI services with mobile applications, designing reliable prompt workflows, handling natural language date extraction, and implementing subscriptions using RevenueCat.

What's next for Todobo

The roadmap includes:

  • Smarter recurring reminders
  • Apple Watch support
  • Shared task lists
  • Offline AI processing where possible
  • More intelligent prioritization and organization

The vision is to make Todobo the fastest and easiest way to capture anything you need to remember.

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