Tactical Intelligence
Detailed technical specifications and operational protocols for the Potestas AI hardening layer.
Core Specifications
Security Protocols
Deep dive into our air-gapped deployment architecture and encryption standards.
Model Agnostic
Compatible with GPT-4, Claude, Gemini, and custom fine-tuned open-weight models.
Performance
Sub-12ms latency overhead even at massive scale and high-throughput workloads.
What makes GLBM-X™ different from standard RAG or fine-tuning?
RAG and fine-tuning are static interventions. GLBM-X™ is a dynamic runtime wrapper. It pressure-maps LLM responses in real-time and applies autonomous patches to intercept drift before the user ever sees it.
How does the 'Zero Retraining' claim work?
Potestas AI sits on top of your existing model stack. We don't modify the weights. Instead, we use a proprietary semantic scope layer to enforce boundaries, achieving deterministic reliability without expensive training cycles.
Can Potestas AI be deployed in air-gapped environments?
Yes. Our architecture is designed for isolated and sovereign cloud deployments. The entire hardening engine, including AI COP monitoring agents, can run with zero external network dependency.
What AI models are supported?
We are model-agnostic. Our SDK supports GPT-4, Claude 3, Gemini, Llama 3, Mistral, and custom fine-tuned models via standard API or local inference endpoints.
What is the typical latency overhead?
In most production environments, GLBM-X™ adds less than 12ms (p99) to the response cycle. Our engine is optimized for high-throughput enterprise workloads.
Still have technical questions?
Our deployment specialists are available for technical reviews and architectural deep-dives.