Portfolio
Data Aggregation and Audio Production System
Industry:
Higher Education
Built for educators to analyze research papers and generate instructional audio materials within a secure, offline environment.
Problem
The use of cloud-based AI for academic research often compromises data privacy and incurs significant recurring API costs.
Privacy Risks:
Sensitive research papers and proprietary teaching materials are vulnerable when uploaded to external cloud servers.
Production Bottlenecks:
Manually converting complex research into audio formats for students is a slow, resource-heavy process.
Cost Management:
High-volume document analysis using commercial LLMs leads to unpredictable and scaling API expenses.
Solution
The system provides a fully offline application that integrates local language models with automated audio synthesis.
Private RAG Pipeline:
A locally hosted Retrieval-Augmented Generation system for querying multiple PDFs without an internet connection.
Offline Vector Search:
Uses a local vector database to index and retrieve specific academic context instantly.
Multi-Voice Synthesis:
Integrated Text-to-Speech (TTS) engine that transforms text into high-quality, multi-speaker audio dialogues.
Localized Execution:
Runs entirely on on-premise hardware, ensuring 100% data sovereignty and zero external data leaks.
Tech Stack
Core Logic:
Local Llama models for private text processing.
Data Handling:
Python and Local Vector Search.
Audio Synthesis:
Local TTS engines for voice generation.
Results & Impact
Data Sovereignty:
Achieved total privacy by keeping all research and student data on local hardware.
Zero Operating Costs:
Eliminated monthly API fees and subscription costs through the use of open-source local models.
Automation Speed:
Enabled the instant conversion of research findings into ready-to-use audio teaching materials.
Seamless Research:
Provided a high-speed interface for educators to interact with their entire library of research papers simultaneously.
Generative AI & RAG solutions