Modular RAG System
Developed for
Platform teams assembling retrieval-augmented products without a one-size monolith
The challenge
- Monolithic RAG repos are hard to customize per domain
- Teams reinvent fusion, memory, and routing for every app
- Arabic deployments need modular hooks for dialect and citation behavior
The Solution
A modular RAG architecture that separates search, fusion, memory, routing, prediction, and task adapters so Labs and products can swap components without rewriting the pipeline.
Key capabilities include:
- ◇ Explicit module boundaries for RAG stages
- ◇ Swappable retrieval and fusion strategies
- ◇ Fits reasoning search, edge RAG, and agent grounding
Impact
Accelerates shipping new grounded assistants while keeping research experiments isolatable.
Organization benefits:
- Reuse across multiple PYXON products
- Cleaner experimentation for scientists
- Easier onboarding for new Labs contributors
Tags
Research Team
Meet Our PIs
Discover the principal investigator behind this project and the expertise that made it possible.
Hamza Salem
Head of PYXON Labs
Leads PYXON Labs research across Arabic AI, edge systems, governance, and applied products that ship into real environments.
// Open Vacancies
Join as a Scientist
Join our team working on Modular RAG System. Explore opportunities in machine learning, Arabic NLP, computer vision, edge systems, and applied AI research.
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Submit the same scientist application used on PYXON Labs — tell us about your CV and how you’d contribute to Modular RAG System.
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