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Customer:
Our client is a well-established media and entertainment company based in the UK. They provide digital audio content, radio shows, and podcast production through channels to their listener community. Over the years, they have built a huge library of podcasts, which includes entertainment, lifestyle, business, and news. They also have in-house creators and independent podcasters working with various podcast channels.
They had a loyal audience base that tuned in daily through multiple platforms, including Spotify, Apple Podcasts, and their own website. However, they noticed their podcast was lacking in a personal touch. For editing the podcast and other workflows, they used to do manual audio editing, categorization, and tagging, which were time-consuming and sometimes gave human error.
Hence, the client approached us to create the best cross-platform podcast app that could automate key production tasks, enhance discoverability, and provide a personalized listening experience to users. The client had multiple queries related to mobile app development pricing, such as
- How much does it cost to develop a podcast app
- How long does it take to build a podcast app
- What features should a podcast app have
Overall, the main requirements were to generate deep audience analytics for better insights and ad targeting, and create multilingual versions of podcasts using AI voice cloning and translation to reach international audiences.
A Challenge to Build a Podcase App Platform:
As the company’s content library and listener base started growing, clients faced technical and business challenges. Managing content manually became a major issue. Every podcast episode had to be uploaded, tagged, and categorized by hand; this used to take up several hours each week. This delayed new releases and made the process inefficient. Listeners were receiving generic recommendations. Plus, they did not have a dashboard where they could see which track or podcast is performing well.
The company also wanted to reach international audiences in Europe and the Middle East, but producing multilingual podcasts manually was expensive and time-consuming. In addition, hiring editors, translators, and transcribers for every show was not a good idea.
From a technical perspective, making a podcast app needs to handle a huge amount of audio data. This requires a powerful AI model for transcription, tagging, and analysis. Plus, integrating various AI components like speech-to-text, natural language processing, and machine translation into one system was another challenge. The client also wanted a scalable platform to handle the growing users.
Achieving high-quality, natural-sounding voice cloning in multiple languages required different accents and tones. Lastly, since the app was going to deal with in-house and third-party content, it needed to have strong data security and compliance with copyright and GDPR regulations.
Best AI Audio Streaming App Solution Implemented By Our Team
After understanding the client’s needs and challenges, our team proposed to develop a custom AI-powered cross-platform podcast app that is capable of giving smart insights, automate some workload and handle multiple audio libraries. The goal was to make podcast management simpler, faster, and more data-driven while creating a more personalized experience for listeners.
As a leading United Kingdom trusted app development agency, our research team started by going through some of the great podcast apps in the market.
We worked on automating time-consuming tasks like tagging, transcription, and categorization using AI and NLP models. This eliminated repetitive manual work and ensured accurate metadata for every episode. To keep listeners engaged, we added a machine learning–based recommendation engine to analyze user behavior, such as likes, play duration, and search activity to suggest relevant or trending episodes.
To help the client expand internationally, we developed an AI voice cloning and translation to recreate podcasts in multiple languages and keep the same speaker tone and emotion. A real-time analytics dashboard will give the marketing team detailed insights about the listener demographics, engagement levels, and ad performance.
Combining our strengths in Android app development services in the UK and cross-platform design, we focused on a unified digital experience and built this podcast app using a cloud-native, microservices architecture to prevent data breaches and help the client grow in the future also.
Key Features for Audio Streaming Application Platform:
- Create account: Users who want to listen to podcasts need to create their account using their email or mobile number.
- AI Audio Tagging & Categorization: This will show podcasts by topic, tone, and genre to help listeners search their podcasts by suitable category.
- Automated Transcription: Converts spoken words into text with high accuracy for easier content search, editing, and SEO optimization.
- Personalized Recommendations: We used an ML algorithm same as Netflix to suggest episodes based on listening behavior and preferences.
- Multilingual Voice Cloning & Translation: The listener from another country can also listen to the podcast using the cloning and translation feature.
- Advanced Analytics Dashboard: This offers insights to admins about the listener demographics, engagement patterns, and ad performance.
- Content Management System (CMS): We created a dashboard for creators where they can upload, edit, schedule, and publish episodes across multiple platforms in one place.
Result of AI-powered Podcast App:
After deploying this AI-powered podcast app, the client was able to reach local and international audiences as well as achieve noticeable results.
- The features like AI tagging, transcription, and categorization reduced manual load up to 80%.
- 40% Increase in listener retention due to personalized recommendations.
- New episodes were published 50% faster due to automation and multilingual releases.
- 25% increase in international audience base because of AI voice translation.
- 44% improved performance due to real-time analytics and proper ad placement.
Technologies and Tools:
- AI/ML Frameworks: TensorFlow, PyTorch
- Speech Recognition & NLP: Google Speech-to-Text API, OpenAI Whisper, spaCy
- Translation & Voice Cloning: DeepL API, ElevenLabs Voice AI
- Backend: Node.js, Python (FastAPI)
- Frontend: React Native, React.js
- Database: MongoDB and PostgreSQL
- Cloud & DevOps: AWS, Docker, Kubernetes, CI/CD pipelines
- Analytics & Monitoring: Google Analytics, Mixpanel
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