Agentic AI / Enterprise AI / LLM Infrastructure
Sovereign AI Workbench
Sovereign On-Prem Agentic AI Workbench
A privacy-focused, self-hosted agentic AI workbench exploring local language models, retrieval, multimodal document intelligence, and AI-assisted engineering workflows.
Project overview
An exploration of private, on-premises AI workflows for enterprise environments where data control and isolated deployment matter.
- LLMs
- RAG
- Agentic AI
- Vector Search
- Multimodal AI
- Self-Hosted Inference
Motivation & privacy
The workbench is motivated by enterprise AI use cases that require stronger control over data, model execution, and deployment boundaries than a public API-first workflow can provide.
Architecture
The intended architecture connects a user interface to an agent orchestrator, local LLM inference, a RAG knowledge base, and enterprise tools. Each boundary is designed to keep capabilities composable.
LLM inference & RAG
The project explores open-weight model inference on local GPU infrastructure alongside embedding-based semantic retrieval for enterprise documents.
Agent orchestration
Multi-step workflows bring retrieval, reasoning, and enterprise tools together. The workbench explores controlled orchestration for AI-assisted code review and engineering analysis.
Multimodal processing
A proposed direction covers document and scanned engineering-drawing understanding, with a focus on safely combining visual and textual context.
Current status & next steps
The workbench remains an active exploration. Proposed directions include multimodal engineering-document processing and deployment in isolated or air-gapped environments. These are development goals, not production deployment claims.
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