Speech Coach

The Challenge

Many professionals struggle with public speaking skills but lack access to affordable, personalized coaching. Traditional speech coaching is expensive ($100-300/hour), scheduling is inflexible, and there's no way to practice privately before important presentations. People need real-time feedback on their speaking patterns, but human coaches can't scale to provide instant analysis.

Built as a SaaS product to democratize speech improvement. The platform needed to provide instant AI-driven feedback that's as good as human coaching but available 24/7 at a fraction of the cost. Target users: professionals preparing for presentations, non-native English speakers improving pronunciation, and anyone wanting to develop confident public speaking skills.

Key Constraints

• Real-time speech analysis - feedback must be instant, not batch-processed
• High-quality audio processing - clear voice recordings needed for accurate analysis
• Accurate AI models - speech recognition must handle accents, pace variations, and emotional tone
• Scalable architecture - support thousands of concurrent users analyzing speech
• Privacy-focused - sensitive speech data must be processed securely
• Affordable pricing - must be accessible compared to traditional $100-300/hour coaches

Our Approach

Built a platform with AI-powered speech analysis. Node.js backend processes audio recordings through LLM APIs for speech pattern recognition, providing users instant feedback on pace, clarity, filler words, and emotional tone. PostgreSQL + Redis for fast data access and session management.

Key Technical Decisions

• Next.js for full-stack - fast development, SSR for SEO, API routes for backend logic
• LLM APIs for speech analysis - leveraging state-of-the-art models for speech recognition, pace detection, and sentiment analysis
• Socket.io for real-time feedback - instant progress updates as AI processes speech recordings
• PostgreSQL for structured data - user accounts, speech sessions, progress tracking over time
• Redis for caching - fast session storage and API response caching for better performance

Timeline: 8-12 weeks from MVP to production launch with core features

Implementation

System Architecture

Next.js handles both frontend (React components, Tailwind CSS) and backend (API routes for auth, speech processing). Node.js backend processes audio through LLM API pipeline (transcription → pace analysis → filler word detection → sentiment). PostgreSQL stores user accounts, session history, and analytics. Redis caches frequently accessed data and manages sessions. Socket.io provides real-time progress updates during AI processing.

Technology Stack

Results & Impact

• Made speech coaching accessible at fraction of traditional cost ($100-300/hour → affordable subscription)
• Enabled private, judgment-free practice environment for nervous speakers
• Provided instant feedback loop - users can iterate and improve in real-time
• Scaled to serve 10K+ users simultaneously (impossible with human coaches)
• Helped non-native speakers improve pronunciation and reduce accents
• Supported professionals preparing for high-stakes presentations and interviews

What We Learned

• AI accuracy varies by accent - had to fine-tune models for non-native English speakers, initial models were biased toward American accents.
• Real-time feedback is addictive - Socket.io progress updates significantly improved engagement, users loved seeing analysis happen live.
• Privacy matters for speech data - emphasized secure processing and data deletion options, users are sensitive about recording their voice.
• Progress tracking drives retention - users who could visualize improvement over time (graphs, metrics) had much higher engagement.
• Simple UX wins - initially overcomplicated the interface, user feedback pushed us toward cleaner, more focused experience.
• Audio quality critical - poor quality recordings significantly impact AI accuracy, needed clear guidelines for users on recording environment.