Summary
Dot Square Lab collaborated with a visionary founder in the education sector to develop a powerful AI-driven platform designed to transform the way lecture recordings are transcribed, analyzed, and explored. The platform provides multi-speaker transcription enriched with intelligent analysis capabilities, including summarization, key point extraction, speaker statistics, and an interactive chatbot-based Q&A experience. Built to serve teachers and educators, the solution empowers users to make sense of classroom interactions like never before.
The challenge
The client is Inform(Ed), an education-focused startup founder, sought an innovative solution to process and extract value from audio recordings of school lectures. Existing products lacked the nuanced analysis and interactivity that modern educational environments demand. The goal was clear: create a robust tool that converts raw classroom audio into actionable insights to improve teaching quality and support student learning.
The solution
DSL conducted an initial discovery phase, surveying the competitive landscape and selecting promising speech-to-text APIs. With these insights, we architected and built a complete platform, integrating industry-leading transcription services with cutting-edge large language models. Key features: Multi-speaker diarization: Accurately distinguishes between different speakers using biometric speech models.
- Automatic transcription: High-quality speech-to-text conversion powered by AssemblyAI.
- Speaker role detection: Identification of teachers vs. students in recorded dialogues.
- NLP analysis: LLM-driven summarization, key points extraction, and question classification.
- Smart statistics: Dashboard with speaker participation metrics, question types, and discussion dynamics.
- Semantic & keyword search: Rapid exploration of transcript contents through fuzzy search and contextual matching.
- Interactive chatbot Q&A: Users can ask questions about the transcript and receive grounded, cited answers from an embedded AI assistant.
- Scalable storage & retrieval: All transcripts are embedded into a vector database for future semantic retrieval.
Technical highlights
- Front-end: Built with Next.js and designed in Figma for a clean, accessible user experience.
- Back-end: Python-based microservices leveraging AssemblyAI for transcription and OpenAI models for analysis.
- Data layer: MongoDB Atlas for structured data and vector database integration for semantic search.
- Deployment: Fully containerized services deployed via GCP Cloud Run, supported by GCP Cloud Storage and Artifact Registry.
- Infrastructure as code: Managed entirely through Terraform with CI/CD pipelines orchestrated via GitHub Actions.
Results & value
The client now operates a fully customized platform tailored for classroom transcription and analysis. Teachers and education professionals can:
- Obtain speaker-attributed, accurate transcripts within minutes
- Review summaries and extract key learning points
- Analyze classroom participation by speaker and topic
- Search for exact terms or semantically related content
- Engage with a conversational AI for in-depth exploration
Pricing efficiency: Analyzed on a sample 30-minute recording, the full end-to-end processing cost was approximately $1.10, making the solution both powerful and cost-effective.
Key takeaways
- Tailored AI: Custom-built LLM workflows deliver educational insights beyond generic transcription tools.
- Data-driven insights: Quantitative analysis of lecture dynamics empowers more informed pedagogical decisions.
- Intuitive UX: Teachers access all features from a clean, simple dashboard designed around their workflows.
- Scalable & cloud-native: GCP infrastructure ensures reliable, scalable performance with minimal DevOps overhead.
