AI-Powered Podcast Search
Pioneering Podcast Discovery with Natural Language AI in the Middle East
About Project
A software company from Jordan specializing in digital transformation had a vision to revolutionize media discovery. Our partnership led to the development of a proof-of-concept web platform that simulates a “Chat with Podcast” experience. The system uses advanced AI to process natural language queries, find relevant audio/video content, and highlight the exact timestamps for playback. This innovative approach lets users listen only to the parts that matter, enhancing content discovery and consumption.
Media and Entertainment
BusinessMiddle East
Location
Business Goal
The collaborative goal was to create an intuitive and innovative platform where users could quickly discover and play the most relevant podcast or video segments by simply asking a question. By leveraging AI to understand queries and pinpoint content, the platform aims to bypass traditional, time-consuming searches. This project’s objective is to enhance the user experience by delivering a smart, efficient, and direct way to access media content.
Project Highlights
- Natural Language Search
- Segment-Based Playback
- AI-Powered Transcript Search
- Open-Use Proof of Concept
- Intuitive User Interface
Enhanced Features
Key Challenges
Meaningful Search
Delivering highly relevant search results through a conversational interface was a key challenge, especially with a limited, pre-uploaded media library.
Intuitive User Interface
Designing a user-friendly interface that could support segment-based playback, highlight timestamps, and offer seamless switching within a single card was a major UX challenge.
AI Performance and Infrastructure
A core technical hurdle was ensuring fast, near real-time search performance using AI models and a vector database while maintaining a lightweight infrastructure.
Transcript Accuracy and Context
Handling the accuracy of automated transcripts was a significant challenge, as errors or a lack of contextual meaning could directly impact the quality of matched results.
Our Solutions
AI-Powered Content Discovery
We implemented a powerful AI pipeline combining OpenAI’s Whisper for transcription, Ada for semantic embeddings, and GPT-4 Turbo for natural language queries. This enables precise similarity matching between user questions and transcript segments. As a result, users can instantly access the most relevant parts of a podcast or video, improving content engagement and reducing time spent searching.
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 ensures users can quickly access and control content, proving the viability of the AI-powered playback concept.
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, we achieved fast, low-latency matching for user queries in near real-time. This provides a scalable, responsive search experience without requiring heavy infrastructure, proving the technical feasibility for future growth.
Accurate and Contextual Relevance
To address the challenge of accuracy, we used OpenAI’s Whisper for high-quality transcription and applied post-processing logic to clean the output. By matching user queries at a semantic level, we improved both the accuracy and relevance of the results. This builds user trust and supports the business goal of offering a smarter, more dependable content discovery experience.
Our Approach
Our strategic approach was guided by a modular pipeline methodology that focused on building a scalable foundation. We prioritized efficiency and user experience to deliver a truly innovative proof-of-concept platform.
Strategy
Architecture
Development
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
Key Results
80-90 Reduction in Search Time
End users now have instant access to relevant segments, dramatically reducing search time by moving from manual exploration to a direct, query-based search.95+ Transcription Accuracy
AI-assisted playback is highly reliable with over 95% transcription accuracy and a 100% successful match rate during testing.70 Less Manual Effort
Manual content exploration was reduced by over 70%, improving the user experience with a clean UI and seamless toggles.100 Viability and Scalability
The functional proof-of-concept proves the project's technical and business viability, with a scalable backend ready for future expansion.
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