AI-Powered Podcast Search
AI-Led Chat with Podcast Platform Development
About Project
The client is a software company based in Jordan, specializing in digital transformation. They provide a wide range of solutions, including ERP, CRM, and IT services, leveraging technologies from Microsoft, Salesforce, and Odoo to deliver innovative business platforms.
Media and Entertainment
BusinessMiddle East
Location
Brief
A Jordan-based software company partnered with us to revolutionize media discovery. Our mission was to develop a groundbreaking proof-of-concept web platform that simulates a “Chat with Podcast” experience. This platform uses advanced AI to process natural language queries, find relevant audio/video content, and highlight the exact timestamps for playback. The result is an intuitive, scalable, and secure interface that empowers users to quickly find and listen to the most relevant podcast or video segments, fundamentally enhancing content discovery and consumption.
List of Services Offered
- Natural Language Search
- Segment-Based Playback
- AI-Powered Transcript Search
- Open-Use Proof of Concept
- Intuitive User Interface
Enhanced Features
Features
AI-Powered Content Discovery
A powerful AI pipeline was implemented, combining OpenAI’s Whisper for transcription, Ada for semantic embeddings, and GPT-4 Turbo for natural language queries. This enables precise matching between user questions and transcript segments. As a result, users can instantly access the most relevant parts of a podcast or video, which contributed to an 80-90% reduction in search time.
Intuitive UI and Playback
A minimalist, card-based UI was designed to simplify the user experience. The interface defaults to segment-based playback with a clear toggle to switch to a full episode. Visual highlights on the scrubber and simple controls within each card make interaction lightweight and intuitive. This approach contributes to over 70% less manual effort for content exploration and enhances the overall user experience.
Streamlined Backend Architecture
A streamlined backend using NodeJS was implemented, integrated with a vector database to store and query semantic embeddings efficiently. By using OpenAI’s Ada for lightweight vector generation, fast, low-latency matching was achieved for user queries in near real-time. This provides a scalable, responsive search experience, proving 100% viability and scalability for future growth.
Accurate and Contextual Relevance
To address the challenge of accuracy, OpenAI’s Whisper was used for high-quality transcription, with post-processing logic to clean the output. By matching user queries at a semantic level, the accuracy and relevance of the results were significantly improved. This contributed to a 95%+ transcription accuracy and a 100% successful match rate during internal testing.
Technology Stack
Front-end Technologies
ReactJs
Back-end Technologies
ReactJs
Server
ReactJs
Database
ReactJs
Cloud
ReactJs
Third-Party
ReactJs
Project Management
ReactJs
Framework
ReactJs
Emergency Technology
ReactJs
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