About project
Amenity Analytics is a leading provider of cloud-based analytics tools designed to help businesses extract actionable insights from vast amounts of unstructured text data. By utilizing advanced natural language processing (NLP) and AI technologies, Amenity Analytics enables organizations to make data-driven decisions, uncover trends, and gain deeper understanding from a wide range of textual sources.
Duration
08.2023 – 07.2024
teCHNOLOGIES
React.js, TypeScript, Next.js, Redux, Zustand, Jest.js, Cypress, Shadcn, Chart.js, Node.js, Nest.js, PostgreSQL, CI/CD, AWS, Amazon Transcribe, GPT-4, RAKE, OpenAI's Whisper
Business solutions
- optimized Performance for Enhanced User Experience:
Implemented performance optimization techniques using SSR/SSG and lazy loading principles, resulting in faster page loads and improved responsiveness, which is essential for delivering a seamless user experience in analytics and admin panels. - AI-Powered Meeting and Conversation Insights:
Developed a pipeline that utilizes Amazon Transcribe for accurate conversation capture and transcription during calls and meetings. By integrating GPT-4, the system extracts and highlights key discussion points, such as questions, decisions, and tasks, enabling users to efficiently process critical meeting data in real-time. - advanced Text and Audio Analytics:
Integrated OpenAI’s Whisper to detect emotions (e.g., frustration, excitement, neutrality) from both audio and textual data. This adds valuable emotional context to conversations, enabling businesses to better understand the dynamics of their communications and make more informed decisions. - real-time Keyword and Key Phrase Extraction:
Used RAKE to detect and extract keywords and key phrases relevant to the conversation context. This feature enhances the value of meeting transcriptions by automatically identifying important topics, which can be broadcasted for immediate attention or further analysis.
Implemented solutions
- performance Optimization & UI Improvements:
Focused on optimizing the platform for faster performance by implementing SSR/SSG and lazy loading techniques, which minimized loading times and ensured that only necessary components were loaded for enhanced user interactions. - AI-Powered Meeting Data Processing:
Developed a pipeline to capture, transcribe, and process meeting conversations using Amazon Transcribe. GPT-4 was configured to automatically identify and extract the most relevant sections, such as decisions, tasks, and questions, broadcasting them in real-time to help users stay focused on critical points. - emotion Detection & Text Analysis:
Integrated OpenAI’s Whisper to detect emotions in both audio and text, providing additional insight into the tone and mood of the conversation. This feature helps businesses assess not just the content of meetings, but also the emotional context, which can improve decision-making and team collaboration. - real-time Keyword Detection with RAKE:
Incorporated RAKE for real-time extraction of keywords and key phrases from transcribed conversations. This automated process ensures that important topics are quickly highlighted and made easily accessible, streamlining the analysis of meeting content and increasing productivity.
Roadmap
Performance Optimization and UI Enhancements
08.2023 - 11.2023
- Implement SSR/SSG and lazy loading principles for improved performance and user experience.
- Develop and optimize features related to the admin panel and analytics using UI libraries like Shadcn and Chart.js.
- Establish and integrate automated tests with TypeScript to maintain high project quality.
Real-Time Conversation Capture and Transcription
11.2023 - 02.2024
- Develop and deploy the pipeline to capture and transcribe calls and meetings using Amazon Transcribe.
- Fine-tune the transcription accuracy for varied conversational contexts (meetings, calls, etc.).
- Integrate GPT-4 for extracting and highlighting key points (questions, decisions, tasks) from transcribed data in real-time.
Emotion Detection and Contextual Analytics
02.2024 - 05.2024
- Integrate OpenAI’s Whisper to detect emotional tone (e.g., frustration, excitement, neutrality) from both audio and textual data.
- Enhance emotion detection capabilities and test its impact on improving meeting insights and decision-making.
- Implement RAKE for real-time keyword and key phrase extraction to further improve contextual understanding.
- Conduct end-to-end testing of all features (transcription, GPT-4 insights, emotion detection, keyword extraction).
- Optimize the entire system for scalability, stability, and performance.
- Finalize integration with front-end UI, ensuring smooth presentation of real-time insights and data highlights.
- Ensure full operational deployment and smooth user onboarding for a seamless experience.
Main Functionality
Performance Optimization and Dynamic UI Rendering:
The platform leverages SSR/SSG and lazy loading techniques to optimize performance, ensuring faster load times and efficient rendering of data. This enhances the overall user experience, especially in the admin panel and analytics interfaces.
Real-Time Conversation Transcription and Key Insight Extraction:
The system uses Amazon Transcribe to capture and transcribe conversations during calls and meetings. GPT-4 is integrated to analyze these transcriptions in real-time, extracting and highlighting key points such as questions, decisions, tasks, and other relevant insights for immediate access.
Emotion Detection for Enhanced Contextual Understanding:
OpenAI's Whisper is used to detect emotions (such as frustration, excitement, or neutrality) from both audio and transcribed text. This feature provides valuable context, helping businesses understand not just the content but also the emotional tone of conversations, improving overall decision-making.
Keyword and Key Phrase Extraction:
The RAKE algorithm automatically detects and extracts relevant keywords and key phrases from conversations, enhancing the quality of meeting transcriptions. This enables users to quickly identify and focus on critical topics, improving information accessibility and operational efficiency.
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