Business Challenge
MeetMinutes needed to transform from a basic transcription service into a comprehensive conversation intelligence platform that could extract actionable insights from meeting recordings. The challenge was to build a solution that could:
1. Process and analyze hours of meeting data in multiple languages 2. Extract meaningful insights, action items, and key moments 3. Enable natural language querying of meeting content 4. Operate with high accuracy while respecting data privacy concerns
Typically, this would require multiple specialized teams: NLP experts, backend developers, cloud infrastructure specialists, and data scientists. I delivered the entire solution end-to-end.
My Approach
Instead of treating this as separate technical challenges, I approached it holistically as an integrated system. My mechanical engineering background helped me visualize the entire information flow, from audio signal processing through to insight generation.
I designed and implemented: 1. **Custom LLM Fine-tuning Pipeline**: Fine-tuned proprietary language models on 2,000+ hours of meeting data, achieving near-GPT-4 performance with 96% fewer parameters. 2. **Multimodal Analysis System**: Built an audio-analysis pipeline that extracted non-verbal cues like tone, pace, and emotion - aspects that transcription alone misses. 3. **Multilingual Processing**: Implemented NLP pipelines for generating meeting summaries, action items, and highlights in multiple languages. 4. **Retrieval-Augmented Generation (RAG) System**: Created a modern 'chat with your meetings' system using vector databases and specialized LLM techniques.
Technical Implementation
The core challenge was creating an LLM that could understand the nuances of conversation without requiring massive computing resources. I: - Fine-tuned DistilBERT and Llama 3 models on specialized meeting datasets - Implemented quantization techniques that reduced model size by 87% while preserving 94% of accuracy - Created a custom data augmentation pipeline that improved performance on low-resource languages
I designed and built a scalable backend system using: - **FastAPI**: For high-performance API endpoints - **AWS Infrastructure**: Lambda for serverless processing, EC2 for model hosting, S3 for storage - **Docker**: For containerized deployment across environments - **DynamoDB & MongoDB**: For structured and semi-structured data storage
My experience in business development at BYJU'S helped me structure the backend to prioritize features with the highest business impact, ensuring the platform delivered immediate value while allowing for future expansion.
Perhaps the most technically complex component was the Retrieval-Augmented Generation system that allowed users to have conversations with their meeting history: - Implemented semantic chunking of meeting transcripts for optimal retrieval - Used Pinecone for vector database storage with custom indexing strategy - Applied query rephrasing techniques to improve result relevance - Created a persistent memory mechanism to maintain conversation context
Business Impact
The end-to-end solution delivered significant business value: - **Premium Product Tier**: The conversation intelligence features enabled a new premium tier with 37% higher pricing - **On-Premise Deployment**: Unlike competitor solutions that required sending data to third-party APIs, our solution could be deployed on-premise for enterprise clients with strict data privacy requirements - **Reduced Resource Requirements**: The optimized models could run on standard cloud instances, reducing operational costs by 64% compared to larger models - **International Expansion**: Multilingual capabilities opened new markets, contributing to a 28% increase in global user base
Perhaps most importantly, the platform was demoed at GITEX 2023 Dubai, where I personally represented the company alongside the CEO, leading to several enterprise partnership opportunities.
The Full-Stack Advantage
This project exemplifies the advantage of having one person handle an entire system: 1. **Unified Vision**: Each component was designed with full awareness of how it would integrate with others, eliminating integration challenges 2. **Rapid Iteration**: Changes could be implemented across the stack without coordination delays 3. **Consistent Optimization**: Performance bottlenecks could be addressed wherever they appeared in the system 4. **Business-Technical Alignment**: My dual perspective in business development and technical implementation ensured features directly addressed market needs
For the client, this translated to faster development, more cohesive user experience, and significant cost savings compared to hiring multiple specialists.
