# RESEARCH
Research
Controlled technical experimentation, adversary simulation, and applied artificial intelligence.
| Subject | Description |
|---|---|
| Applied AI & Large Language Models | Evaluating reasoning limits, prompt injection vulnerabilities, and decision boundaries of autonomous agents. |
| Adversary Simulation & Exploitation | Researching zero-day primitives, memory corruption dynamics, and custom exploit harnesses. |
| Identity Attribution & Surveillance | Analyzing commercial ad-tracking identifiers, mobile IDs, and persistent device fingerprinting. |
| Sovereign & Air-Gapped Systems | Hardened local inference, verified data boundaries, and isolated operations without external dependencies. |
| Title | Description | Project Stage of the Lifecycle |
|---|---|---|
| Private Cyber Trained LLM | This project is testing the effectiveness of utilizing medium sized LLM's under 30B parameters using 4 to 6 quantization settings for their ability to effectively and efficiently conduct offensive and defensive network and system analysis. |
Testing Phase
Phase 3 of 5 • Est. Q1 2027
|
| SignalHunter | Multi-spectrum RF and sensor suite scanning Wi-Fi (2.4/5/6 GHz), Bluetooth/BLE, Cellular towers, Sub-GHz, NFC, EMF, and optical IR/lens reflections in real time. Detects hidden wireless and pinhole cameras, covert audio bugs, unauthorized BLE trackers (AirTag, SmartTag, Tile), rogue IMSI-catcher (Stingray) base stations, and active RF jammers. |
Testing
Phase 4 of 5 • Est. Q4 2026
|
| Quantitative Learning Enhancement | Quantitative learning enhances Large Language Models by integrating mathematical optimization, statistical calibration, and numerical reward modeling directly into training and inference pipelines. Through precision-aware weight quantization and structured reinforcement feedback, models achieve higher multi-step reasoning accuracy while significantly reducing memory footprints and compute latency. It further grounds probabilistic token generation with deterministic verification gates, minimizing hallucinations across complex analytical, cryptographic, and telemetry workloads. This enables compact, locally deployed LLMs to match or exceed the analytical rigor of massive frontier models in resource-constrained environments. |
Testing Phase
Phase 2 of 5 • Est. Q3 2027
|