R&D Infrastructure
Production sovereign AI infrastructure — not a laptop running notebooks. Multi-GPU training, local inference, vector search, and a complete offline software stack.
Design Philosophy
Our R&D infrastructure is built on one principle: "If it can't run without the internet, it shouldn't be in the stack." Every component operates on local hardware. No external API calls. No cloud model access. No data leaving the building. This isn't a preference — it's the architecture our R&D divisions require for medical, educational, and law enforcement research where data sovereignty is mandatory.
This infrastructure is not theoretical. It runs our business today. Request a tour to see real monitoring dashboards, real AI systems, real security.
Hardware Stack
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Training GPU
NVIDIA / CUDA
High-VRAM NVIDIA GPU dedicated to model training and research. Handles LoRA/QLoRA fine-tuning, generative pipelines, and multimodal R&D. Specific hardware is configurable per vendor and deployment requirements.
- LoRA/QLoRA model fine-tuning
- Generative model research and testing
- Multimodal model research (vision-language)
- Custom model training pipelines
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Inference GPU
NVIDIA / CUDA
Dedicated NVIDIA GPU for local model serving and inference. Handles research queries, RAG pipelines, and development testing. Isolated from the training GPU to eliminate resource contention.
- Local model serving (offline inference)
- RAG inference queries
- Development and testing inference
- Isolated from training GPU (no contention)
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Host System
Linux
Primary R&D workstation running the complete AI software stack. Containerized service orchestration, full development environment, air-gapped deployment capability.
- Containerized service orchestration (12+ services)
- Complete AI software stack pre-installed
- Air-gapped / offline-capable operation
- Decentralized infrastructure ready
Software Stack
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Model Serving
- Local model inference (NVIDIA CUDA, offline)
- Multiple model sizes supported
- OpenAI-compatible API (local only, no external access)
- Custom GPU assignment (GPU isolation for training)
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Vector Search
- Local vector database (sovereign, no external APIs)
- API-key authenticated access
- Sovereign embedding computation (no external embedding APIs)
- Collections for document, image, and multimodal vectors
- Daily snapshots for data durability
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Training Pipeline
- LoRA/QLoRA fine-tuning on NVIDIA CUDA GPUs
- Generative model training and testing
- Custom training dataset management
- Model evaluation and metrics pipeline
- Quantization pipeline for model compression
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RAG Pipeline
- Document ingestion (PDF, DOCX, TXT, email, images)
- Local embedding computation (no external API)
- Local vector storage and retrieval
- Natural language query interface
- Citation and source tracking
- Access control with audit logging
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Security & Monitoring
- SIEM security monitoring (standalone deployment)
- Vulnerability detection enabled
- System health monitoring dashboards
- Container auto-restart policies
- Network-level DNS filtering
- Automated backup rotation
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Backup & Durability
- Hourly mirror backup
- Weekly compressed archives (4-week retention)
- Monthly golden archives (3-6 month retention)
- Vector database daily snapshots
- Multi-tier backup storage
What This Means for R&D
This infrastructure enables research that cloud-dependent labs cannot perform:
- HIPAA-compliant training: Fine-tune models on medical data without any data leaving the hardware.
- FERPA-compliant education research: Train and evaluate K-12 AI systems on student interaction data that never leaves the school network.
- Classified-capable law enforcement R&D: Air-gapped deployment means research can proceed on sensitive law enforcement data without external connectivity.
- Reproducible experiments: Controlled hardware, fixed software versions, no cloud API changes between runs. Results are reproducible.
- No vendor dependency: Model selection, training data, inference parameters, and update timing are all under our control — not a cloud provider's.
- Cost predictability: Zero per-query API costs. Zero per-token charges. Zero surprise cloud bills. The infrastructure cost is fixed and known.
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