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.
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 →