AI SolutionsR&D Project
Local Voice-to-RAG Memory System
A personal R&D build: a local-first system converting voice recordings into a queryable RAG-indexed memory store, exploring fully offline retrieval-augmented architecture.
The Challenge
What needed to be solved
Explored whether a fully local, privacy-preserving RAG pipeline could reliably capture and surface meeting or seminar content on natural-language query, with zero cloud dependency.
What We Built
Built a local bot that continuously transcribes voice input and indexes it into a RAG-based memory layer, allowing conversational query over any past session — entirely offline.
Gallery
Project Screenshots
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Stack Used
Technologies & Tools
WhisperVector DatabaseRAG PipelinePython
Outcomes
Results & Impact
Local
Zero Cloud Dependency
Fully offline execution — no data leaves the device.
R&D
Offline RAG Proving Ground
Validates architecture used in privacy-sensitive client engagements.
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