GLBM-X™ Technology
A proprietary runtime wrapper that intercepts, pressure-maps, and autonomously patches LLM outputs — no retraining, no vendor lock-in.
What GLBM-X™ Delivers
Pressure Mapping
Continuously maps adversarial pressure vectors identified during Katana audits. Each pressure point becomes a monitored checkpoint at runtime.
Autonomous Self-Patching
When drift or failure is detected, the patch engine applies pre-validated correction without human intervention or service interruption.
Semantic Scope Enforcement
Enforces defined semantic boundaries — the engine that achieves 0% error rate within scoped task domains.
10M Token Context Recall
Maintains coherent recall across 10M scoped tokens with 13-hop traversal — far beyond standard context window limitations.
Air-Gapped Deployment
Fully functional without external network access. Designed for isolated environments and sovereign cloud deployments.
Universal Model Compatibility
Works with GPT-4, Claude, Gemini, Llama, Mistral, and custom fine-tuned models. API-first design with drop-in SDK integration.
Runtime Intervention.
Zero Retraining.
GLBM-X™ sits as a transparent wrapper between your application and the underlying LLM. Every prompt and response passes through its pressure-mapping engine, which identifies drift vectors in real time and applies pre-computed correction patches autonomously.
Unlike fine-tuning or RAG retrofits, GLBM-X™ requires no modification to the base model. It works with any LLM — proprietary or open-weight — and is fully compatible with air-gapped deployments.
Request Integration DetailsBefore & After GLBM-X™
| Metric | Baseline (Unmitigated) | With GLBM-X™ | Improvement |
|---|---|---|---|
| Hallucination Rate | ~45–60% | ~7–9% | ↓ 85% |
| Semantic Error Rate (Scoped) | Variable | 0% | ↓ 100% |
| Adversarial Prompt Resistance | Low | High | ↑ Significant |
| Context Coherence (Long-Window) | Degrades after 32K tokens | Stable to 10M scoped tokens | ↑ 300x+ |
| Latency Overhead | N/A | <12ms p99 | Negligible |
* Results based on internal adversarial testing across 7.5M+ turns. Individual results vary by deployment scope.
Works With Your Stack
Why Not Just Fine-Tune?
Fine-Tuning
- Requires large labeled datasets
- Weeks of training cycles
- Model-specific — must redo per vendor
- Cannot patch zero-day vulnerabilities
- Degrades over time without retraining
RAG Retrofits
- Adds latency and complexity
- Knowledge cutoff still applies
- Doesn't address reasoning failures
- Hallucination rate remains high
- Requires ongoing corpus maintenance
Runtime Hardening
- Zero retraining required
- Works with any model instantly
- <12ms latency overhead
- Patches vulnerabilities in real time
- Autonomous self-correction at runtime
Operational in Days, Not Months
Integration
Drop-in SDK connects to your existing LLM pipeline. API proxy or direct integration — your choice. No model access required.
Calibration
GLBM-X™ maps your model's pressure points using Katana test vectors. Correction patches are pre-computed and validated against your specific use case.
Autonomous Operation
The system runs independently. Pressure maps update continuously. Self-patching activates when drift is detected. AI COP agents monitor 24/7.
Hallucination Intercepted in Production
Deploy GLBM-X™ in Your Environment
Integration takes hours, not weeks. Air-gapped or cloud-connected — we work with your constraints.