The Cognitive Process
COORDINATOR acts like a brain, processing and analyzing data to make informed decisions.
1. OBSERVE
Multimodal sensory cortex ingesting real-time data streams through MCP tools—live APIs, computer vision pipelines, and vector-indexed knowledge repositories. Continuous state monitoring with reduced latency.
Capture · Analyze · Filter · Process
2. THINK
Dual-mode cognitive architecture mirroring Slow/Fast processing—fast heuristic inference for urgent audibles, deep chain-of-thought reasoning for strategic planning. Attention mechanisms weighted by situational context embeddings.
Monitor · Reason · Predict · Recommend
3. ACTION4. VERIFY
Strategic execution model bridging cognition and implementation—instant tactical adjustments via tool-augmented generation, comprehensive game plans through hierarchical planning layers. Scalable intelligence deployment across coaching staff interfaces.Reinforcement learning mechanism with temporal difference updates— comparing predicted vs. actual outcomes. Dual memory: episodic buffer for immediate adaptation, vector database for long-term strategic evolution and opponent modeling.
Compare · Learn · Adjust · Evolve
4. VERIFY3. ACTION
Reinforcement learning mechanism with temporal difference updates— comparing predicted vs. actual outcomes. Dual memory: episodic buffer for immediate adaptation, vector database for long-term strategic evolution and opponent modeling.Strategic execution model bridging cognition and implementation—instant tactical adjustments via tool-augmented generation, comprehensive game plans throughhierarchical planning layers. Scalable intelligence deployment across coaching staff interfaces.
Plan · Execute · Adapt · Scale
Sleep/Wake Architecture
A biomimetic learning cycle that mirrors neural consolidation— active inference during wake, adaptive training during rest.
WAKE
The user inference executing a fine-tuned NVIDIA Nemotron model with minimal latency. Real-time strategic analysis, pre, live and post game-state evaluation, pattern and trend recognition, and instant tactical recommendations.
Base model weights remain immutable during active deployment
Sub-second response times for critical in-game adjustments
Full computational resources allocated to inference
SLEEP
Low-Rank Adaptation (LoRA) training phase applying lightweight adapters to a NVIDIA Nemotron foundation model. Efficient fine-tuning on game-specific data—post-game analysis, opponent tendencies, situational patterns—without catastrophic forgetting or hallucination.
LoRA matrices decompose weight updates into low-rank factors
Training completes in hours, not days—perfect for weekly game prep
Swap LoRA adapters per opponent without retraining base model
Technical Implementation
LoRA injects trainable rank decomposition matrices into Nemotron attention layers while freezing pretrained weights
Reduces trainable parameters while maintaining close-to-full fine-tuning performance
LoRA adapters merge into base weights at deployment—zero latency penalty during inference
Security In Mind
Built with security-first architecture to protect your competitive advantage.
Edge Deployment
Runs entirely on NVIDIA AI computing hardware with zero cloud dependencies. Air-gapped infrastructure prevents opponent reconnaissance while maximizing inference throughput via tensor core acceleration.
Offline LoRA Training
All model adaptations use parameter-efficient fine-tuning in isolated environments. Strategic insights remain encrypted at rest and are never transmitted to external cloud services.
Role-Based Access Control
Multi-tier authentication with hardware security modules. Granular permissions ensure coordinators access full model outputs while position coaches see filtered, role-specific insights.
Zero-Knowledge Architecture
All game plans stored in locally-encrypted vector databases with per-session key rotation. Data remains inaccessible outside your facility's physical perimeter and authorized personnel only.
In Development
Stay tuned for updates on COORDINATOR's development and insights.