TutorPro: AI-Orchestrated SaaS Development

TutorPro is a professional tutoring management platform—and a case study in what becomes possible when you orchestrate multiple AI agents across your development workflow.

The Thesis

Traditional solo development faces a hard ceiling: one person, one pair of hands, one typing speed. But what if you could expand that? What if you could delegate entire features to specialized AI agents while maintaining human oversight and quality control?

TutorPro is the result of testing this hypothesis at scale. Built over the course of weeks using orchestrated cloud and local AI agents, it demonstrates that a solo developer with the right process can move at 10-20x the velocity of traditional development—without sacrificing code quality or architectural integrity.

The Architecture: Multi-Tenant SaaS

TutorPro serves two primary audiences:

For Tutors

• Dashboard: Real-time session overview, student statistics, recent reviews
• Calendar Management: Intuitive scheduling and session rescheduling
• Student Management: Detailed profiles, progress tracking, notes, and session history
• Professional Showcase: Beautiful landing pages to attract new students and parents

For Parents & Students

• Progress Tracking: View notes and progress updates from tutors
• Session Booking: Self-service scheduling with tutors
• Real-time Messaging: 1-on-1 and group conversations with message attachments
• Reviews & Ratings: Leave feedback and track tutor qualifications

Platform-Level Features

• Multi-tenant Architecture: Isolated data per tutor with shared infrastructure
• Responsive Design: Desktop, tablet, and mobile—seamlessly
• Real-time Updates: Firebase Firestore for live data synchronization
• Cloud Functions: Serverless logic for messaging, notifications, and data management
• Comprehensive Testing: Local Firebase emulators + automated test environment setup

The Tech Stack

Frontend

• React 18 with TypeScript for type-safe, component-driven UI
• Vite for fast builds and hot module replacement during development
• Tailwind CSS for utility-first styling and responsive design
• Lucide React for a consistent icon library
• Vitest + React Testing Library for component and integration testing

Backend & Infrastructure

• Firebase Authentication for OAuth and multi-factor auth
• Firestore for real-time document-oriented data
• Cloud Functions for serverless backend logic (messaging, notifications, triggers)
• Cloud Storage for message attachments and user-generated content
• Firebase Emulators for local development with production parity

Developer Experience

• Docker & Docker Compose for containerized deployments
• GitHub Actions for automated testing and CI/CD
• Firebase CLI for local development and deployments
• Comprehensive npm scripts for every workflow (setup, test, build, deploy)

The Development Process: AI Agents at Scale

This is where TutorPro becomes interesting. The platform wasn't built in the traditional way. It's a practical demonstration of the AI agent orchestration workflow documented in detail elsewhere—but let's highlight how it works here.

Phase 1: Specification & Planning

Features start as detailed specs written with Claude (or Claude Code Web). The key: ruthless chunking. Each task is broken into 4-8 hour implementation units. Too large, and agents choke or hallucinate. Too small, and you lose efficiency. The right granularity is everything.

Specs become GitHub Issues—that's the command center.

Phase 2: Cloud Agent Implementation

This is where Claude Code Web shines. Issues get assigned to Claude, which:

1. Reads the repository context
2. Analyzes the existing codebase
3. Implements the feature end-to-end
4. Runs linting and tests locally
5. Submits a pull request

Wake up the next morning to 80-90% production-ready code.

GitHub Copilot handles specific types of tasks well too—especially context-aware code generation within a single file or quick iterations.

Phase 3: Local Agent Refinement

Gemini CLI and Claude via CLI handle:

• Rapid prototyping and iteration
• Architectural discussions
• Design decisions
• Local testing before cloud deployment

Phase 4: Quality Gates & Review

Pull requests from cloud agents get reviewed by:

• Automated linting and testing (GitHub Actions)
• Additional AI passes (via Claude or Copilot in PR comments)
• Human review (me, maintaining final say on quality)

This creates multiple layers of quality assurance without becoming a bottleneck.

Phase 5: Deployment

Tagged releases (rel_v1.0.0) trigger automated Firebase deployments via GitHub Actions. Build → test → deploy. Zero-touch to production.

Key Features That Showcase the Stack

Real-Time Messaging System

A full-featured messaging system with:

• 1-on-1 and group conversations
• Message attachments (PDFs, images, documents)
• Message actions (send, unsend, delete, forward)
• Read status indicators and unread counts
• Email notifications
• User search with disambiguation
• Cloud Functions handling attachment uploads and notifications

This feature alone would take weeks in a traditional team. With agent orchestration, it was built, tested, and deployed in days.

Calendar & Session Management

• Monthly calendar views with color-coded session types
• Drag-to-reschedule functionality
• Session details sidebar
• Real-time sync via Firestore
• Mobile-responsive design

Test Environment Automation

The project includes comprehensive test data setup:

• 6 pre-configured test users (tutors, parents, students)
• Multiple relationships and student records
• 10+ invitations with various statuses
• Sample session history
• 3 messaging conversations with 15+ messages
• Sample attachments for upload testing

One command: npm run setup-test-env. Everything is ready.

Developer Velocity in Numbers

Without agent orchestration, building TutorPro's scope would require:

• Estimated solo dev time: 9-12 months of full-time work
• Estimated small team: 3-5 people, 3-4 months

Actual development: Weeks of part-time work, mostly orchestration, review and oversight.

The velocity multiplier isn't just about speed—it's about type of speed. You're not faster at typing; you're faster at deciding, reviewing, and iterating. The AI handles the mechanical parts; the human maintains the vision.

AI Cloud Agents Integration

TutorPro is deeply integrated with a number of cloud based AI agents:

• SessionStart hooks auto-install dependencies on every session
• Pre-approved permissions for npm, firebase, and git commands
• Custom commands for domain-specific tasks
• Environment setup that configures Firebase emulators automatically

This means AI cloud agents can go from fresh clone to fully running dev environment in seconds.

Infrastructure & Deployment

Local Development

npm run local  # Starts Firebase emulators + Vite dev server

Hits http://localhost:5173 with full Firebase emulator support.

Docker

docker-compose up  # Full containerized environment

Production

Tagged releases trigger GitHub Actions → Production Firebase deploy. Fully automated.

What This Means

TutorPro proves something important: developer velocity is no longer a function of coding speed. It's a function of:

1. Process clarity (specs, chunking, clean issue boundaries)
2. Tool selection (which AI for which task)
3. Quality gates (automated + human review)
4. Willingness to experiment (constantly optimizing the workflow)

A solo developer using the right orchestration can move faster than small teams. The leverage isn't in typing—it's in orchestration.

Future Enhancements

• Payment Processing: Integrated billing and payment handling
• Video Calling: Live sessions with screen sharing
• Mobile App: Native iOS and Android
• Advanced Analytics: Detailed reporting and insights
• AI-Powered Features: Smart scheduling and progress predictions

The Real Takeaway

TutorPro isn't just a tutoring platform. It's a proof of concept for what's possible when:

• You break work into manageable specs
• You assign tasks to the right AI agent
• You maintain human judgment at quality gates
• You automate everything else

The result? Software that would've taken months, built in weeks. Not through magic. Through orchestration.

Learnings

• Spec quality matters more than coding speed. A 30-minute spec saves days of agent confusion.
• Different agents excel at different tasks. Claude for architecture, Copilot for context-aware edits, Gemini for quick iterations.
• UI development is hard. The difficulty to guide good UI design has proven to be frustrating at times.
• Local emulators are essential. Testing agent code locally before deployment catches hallucinations early.
• Human review remains critical. Agents are great scaffolding; humans provide judgment and vision.
• Process > Tools. The right process with mediocre tools beats great tools with chaotic process.

Want to see the process in action? Check out the full AI agent orchestration workflow that made this possible.

Curious about TutorPro? Visit tutorpro.kids