AI meeting assistants have become one of the most practical applications of modern AI. Tools can now transcribe conversations, generate summaries, extract action items, and help teams keep track of decisions made during meetings.
As developers, it's easy to look at products like Otter, Fireflies, Fathom, or Read AI and assume they are incredibly complex systems that require large teams and massive infrastructure.
The reality is that the core functionality of an AI meeting assistant can be broken down into a surprisingly small set of components.
That's exactly what I'm building: a minimal AI Meeting Assistant that focuses only on the essential features and is developed completely in the open.
The goal is not to compete with enterprise products. The goal is to learn, experiment, and create an educational open-source project that demonstrates how modern AI meeting assistants actually work.
In this article, I'll explain the project scope, architecture, technology choices, and development roadmap.
Why Build a Minimal AI Meeting Assistant?
Most AI meeting assistants provide dozens of features:
Meeting bots
Calendar integrations
CRM synchronization
Analytics dashboards
Sentiment analysis
Team workspaces
Workflow automation
While these features are useful, they can also obscure the core problem being solved.
At its heart, an AI meeting assistant only needs to perform a few tasks:
Capture meeting audio
Convert speech to text
Generate a summary
Extract decisions
Extract action items
Export the notes
Everything else is optional.
By focusing on the fundamentals, we can better understand the architecture and technologies involved.
Project Goals
The project has three primary goals:
- Learn by Building
Rather than consuming tutorials, I want to understand how each component works by implementing it myself.
- Create an Open-Source Reference Project
Every step will be published on GitHub so other developers can follow along, experiment, and contribute.
- Document the Journey
Every major milestone will become a technical article covering:
Architecture decisions
Implementation details
Challenges encountered
Lessons learned
Defining the MVP
Before writing code, it's important to define what the first version will include.
Included Features
✅ Create meetings
✅ Record audio
✅ Upload audio files
✅ Generate transcripts
✅ Generate meeting summaries
✅ Extract decisions
✅ Extract action items
✅ Export notes
Excluded Features
❌ Zoom integrations
❌ Teams integrations
❌ CRM integrations
❌ Sentiment analysis
❌ AI agents
❌ Team collaboration
❌ Analytics dashboards
❌ Mobile apps
Keeping the scope small reduces complexity and increases the chance of actually shipping a working product.
Technology Stack
The project uses technologies that are widely adopted and developer-friendly.
Frontend
React
TypeScript
Tailwind CSS
React provides a flexible component-based architecture while TypeScript improves maintainability as the project grows.
Backend
Python
FastAPI
FastAPI has become one of the most popular frameworks for AI-powered applications due to its:
Excellent performance
Automatic API documentation
Type safety
Async support
Database
SQLite
SQLite is more than sufficient for the initial version and keeps deployment simple.
AI Components
Future phases will introduce:
Whisper
Large Language Models
FFmpeg
These components will power transcription and meeting intelligence.
System Architecture
The architecture is intentionally simple.
React Frontend
|
v
FastAPI Backend
|
+-------------------+
| |
v v
Audio Services AI Services
| |
v v
Transcription Summaries
Decisions
Action Items
|
v
SQLite Database
Each service has a clearly defined responsibility.
This modular approach allows components to evolve independently.
Repository Structure
The repository will use a monorepo approach.
minimal-ai-meeting-assistant/
│
├── frontend/
│
├── backend/
│ ├── app/
│ │ ├── api/
│ │ ├── models/
│ │ ├── schemas/
│ │ ├── services/
│ │ └── providers/
│
├── docs/
├── tests/
├── storage/
│
├── docker-compose.yml
├── .env.example
└── README.md
The structure is designed to support incremental growth while remaining easy to navigate.
Core Data Model
The initial database schema is intentionally minimal.
Meeting
id
title
created_at
status
audio_path
Transcript Segment
id
meeting_id
speaker
start_time
end_time
text
Summary
id
meeting_id
overview
Action Item
id
meeting_id
task
owner
due_date
status
Decision
id
meeting_id
decision_text
These entities cover the majority of meeting-related workflows.
The Transcription Pipeline
The first AI-powered component will be transcription.
The workflow looks like this:
Audio Upload
|
v
Audio Validation
|
v
Audio Processing
|
v
Speech-to-Text
|
v
Transcript Storage
Expected transcript output:
{
"start": 12.4,
"end": 15.9,
"speaker": null,
"text": "Let's finish the prototype by Friday."
}
Timestamps are important because they allow summaries and action items to be traced back to the original conversation.
Structured AI Outputs
One common mistake when working with LLMs is generating unstructured text.
For meeting intelligence, structured outputs are much more useful.
Example:
{
"summary": "The team reviewed the project timeline.",
"decisions": [
"Testing will begin next week."
],
"action_items": [
{
"task": "Prepare test environment",
"owner": "Sarah"
}
]
}
Structured responses are easier to validate, store, display, and edit.
Building in Public
One of the most interesting aspects of this project is that every stage will be documented publicly.
That includes:
Successes
Failures
Architectural changes
Performance issues
Development mistakes
Too many technical tutorials only show the final solution.
Real-world development is much messier.
I believe documenting the entire process is more valuable than only showing polished results.
Development Roadmap
The project will be built in phases.
Phase 1
Project setup
Phase 2
Meeting creation
Phase 3
Audio recording and uploads
Phase 4
Transcription
Phase 5
Summary generation
Phase 6
Decision extraction
Phase 7
Action-item extraction
Phase 8
Exports
Phase 9
Testing and deployment
Each phase will be released as a working milestone.
What I Hope to Learn
Some of the questions I want to answer include:
How accurate is modern speech-to-text?
How reliable are AI-generated action items?
What meeting information is most difficult to summarize?
What is the real cost of processing meetings?
Which features provide the most value?
The project is as much an experiment as it is a software application.
Conclusion
AI meeting assistants are often viewed as complex enterprise products, but their core functionality can be reduced to a small set of building blocks.
By focusing on:
Audio capture
Speech-to-text
Summarization
Decision extraction
Action-item extraction
we can build a useful AI application while gaining a deeper understanding of the technologies involved.
Over the coming weeks, I'll be implementing each component, publishing the code on GitHub, and sharing the lessons learned along the way.
If you're interested in AI engineering, FastAPI, React, speech-to-text systems, or building practical AI applications, follow the project and join the journey.
The first commit is just the beginning.