BATEN
Augmented Intelligence Engine
What if LLMs were governed by physics, not hope?
A Rust-native engine that applies gravitational fields, torsion mechanics,
Shannon entropy, and formal observation algebra to deterministically steer
any Large Language Model. No hallucinations. No cloud. No retraining.
3
International PCT Filings
65,536
Max Hilbert Dimension
Start with the Engine
Physics-governed AI running locally. No hallucinations. No cloud dependency.
The engine is built. Going live April–May 2026.
Open Cockpit →
Σ
Behavioral Engine
Emergent behavioral profiles selected by geometric distance in state space. Purely deterministic. Same state = same identity.
⟨ψ⟩
Quantum Core
Intent decomposition in high-dimensional vector spaces (Q4 to Q65536). Entropy-weighted collapse. Reproducible for any given input state.
↺
Autonomous Steering
Real-time semantic friction monitoring. Automatic correction when output drifts from expected state. No model modification required.
𝓕
Causal Data Geometry
Every data block carries intrinsic causal geometry. SHA-256 proof of state. Observer-relative visibility at the structural level.
◈
Semantic DSL
Purpose-built domain-specific language for semantic computation. Rust-native lexer-parser-runner. NDJSON audit trail.
⚡
Model Agnostic
Works with Mixtral, Llama, DeepSeek, or any LLM. Same physics pipeline. Offline-first. Full data sovereignty.
BATEN vs. Conventional Approaches
Aspect
Prompt Engineering
RLHF / Fine-Tuning
BATEN Engine
Auditable
No
Partial
Full trail
Model-agnostic
Yes
No
Yes
Offline
Depends
Depends
100%
Real-time correction
No
No
<1 ms
Requires retraining
No
Yes
Never
Technology Stack
Backend: Rust (multi-crate workspace). Tauri 2 for native desktop. Zero-copy IPC.
Frontend: React / TypeScript. Real-time canvas instrumentation at 60 FPS.
Algebra: high-dimensional vector core (Q4–Q65536), semantic computation DSL.
Data: causal geometry layer. SHA-256 causal chains. Observer-relative replay.
LLMs: Ollama proxy (Mixtral 8x7B, Mistral 7B, Llama 3 8B, Llama 3.1 70B, DeepSeek V3).
▸ Read the full technical deep dive →
Who Is This For
Enterprise teams deploying LLMs who need auditability, reproducibility, and zero-hallucination guarantees.
Regulated industries (legal, medical, finance) requiring deterministic output and full decision trails.
AI researchers interested in physics-based approaches to LLM governance.
Investors and partners looking at the next infrastructure layer for reliable AI.
Start with the Engine
Physics-governed AI running locally. No hallucinations. No cloud dependency.
The engine is built. Going live April–May 2026.
Open Cockpit →