Zero-Day Detection Capability
100% Data Coverage
Multi-Anomaly Detection
Licensable Detection Engine - Validated on Public Benchmarks

EmbedWorld-Class DetectionInto Your Product

Triaxis AI Core Engine enhances, augments, or replaces anomaly detection in your existing products. Orders of magnitude faster. No GPU required. No training. License our engine to improve speed, accuracy, and reduce infrastructure costs.

Why License Triaxis AI

100x+
Speed Improvement
vs traditional ML pipelines
72-99%
Accuracy Range
Across benchmark datasets
$0
GPU Required
CPU-only performance
Zero
Config Needed
No parameters to tune

AI You Can Actually Audit

Triaxis AI fuses three branches of AI - unsupervised clustering, geometric analysis, and statistical learning - into a deterministic system. No GPUs, no training data, no black-box opacity.

ReproducibleSame input = same output
AuditableFull decision trail
No HallucinationsMath, not guessing
Regulation-ReadyPasses compliance
Request Trial LicenseSee Integration Example
Triaxis AI CORE - One Algorithm, Zero Configuration, Universal Detection across Banking, Manufacturing, Energy, Healthcare, IoT, Cybersecurity, AI Safety, and Retail
Discover More
One Algorithm, Proven Results

Validated onPublic Benchmark Datasets

The same geometric detection engine achieves exceptional results across network security, manufacturing, finance, healthcare, and beyond. Zero configuration changes between datasets.

Hover over cards to see detailed results

Traditional ML approaches require months of setup, labeled data, hyperparameter tuning, and specialized hardware. Triaxis AI deploys in 5 minutes with zero configuration on standard hardware.

Blockchain AML

99.1%
BEATS ALL SOTA
31,184/sec

Blockchain AML

Elliptic Bitcoin (166D, 6K samples)

Achieves 99.1% F1 at 31,184 transactions/sec on blockchain AML detection. Graph-derived features capture transaction patterns across the Bitcoin network.

Precision
98.1%
Cohen's d
16.82
CHAMPIONDetails

Manufacturing

93.0%
Matches Deep Learning
34,887/sec

Manufacturing

Tennessee Eastman (52D, 13K samples)

Matches deep learning performance without training or labeled fault data. At 34,887 samples/sec, monitor industrial processes in real-time on edge hardware.

Precision
98.3%
Cohen's d
2.26
#1 UNSUPERVISEDDetails

Smart Grid

90.6%
BEATS ALL SOTA
259,643/sec

Smart Grid

Household Power (7D, 175K samples)

Achieves 99.2% precision detecting appliance failures and unusual consumption patterns. At 259,643 samples/sec, a single CPU instance can monitor 100,000+ smart meters.

Precision
99.2%
Cohen's d
1.63
CHAMPIONDetails

LLM Hallucination

83.2%
Beats GPT-4
18,057/sec

LLM Hallucination

HaluEval (384D, 1K Q/A pairs)

Achieves 83.2% F1 at 18,057 Q/A pairs/sec on LLM hallucination detection. Angular coherence analysis detects semantic drift without training data.

Precision
89.4%
Cohen's d
-2.04
CHAMPIONDetails

Network Security

72.0%
Real-time Detection
12,823/sec

Network Security

CTU-13 Botnet (384D, 76K flows)

Achieves 72.0% F1 at 12,823 flows/sec for network intrusion detection, enabling real-time PCAP analysis on CPU alone. Multi-Stage refinement reduces false positives.

Precision
69.6%
Cohen's d
1.93
STRONGDetails

Insider Threat

89.7%
100% Precision
6,627/sec

Insider Threat

CERT UEBA (768D, 1.2K users)

Near-perfect 100.0% precision minimizes false positives for SOC teams. At 6,627 users/sec, monitor entire enterprise workforce in real-time.

Precision
100.0%
Cohen's d
2.81
COMPETITIVEDetails

High-Frequency Trading

80.4%
99.1% Precision
115,231/sec

High-Frequency Trading

FI-2010 LOB (40D, 15.5K events)

Achieves 80.4% F1 with 99.1% precision at 115,231 samples/sec on limit order book anomaly detection. High precision enables reliable real-time alerts.

Precision
99.1%
Cohen's d
1.83
COMPETITIVEDetails

Audio Anomaly

74.3%
88% Precision
5,228/sec

Audio Anomaly

ESC-50 (512D CLAP, 1.2K samples)

Achieves 74.3% F1 with 88.0% precision on unsupervised audio anomaly detection. At 5,228 samples/sec, enables real-time environmental monitoring.

Precision
88.0%
Cohen's d
1.17
STRONGDetails
8
Benchmark Datasets
84.7K
Avg Samples/Sec
0
Configuration Required
100%
CPU-Only Processing
Technical Foundation

How It Works

Three core innovations enable universal anomaly detection without configuration

Geometric Principle

Natural Cluster Discovery

High-dimensional embeddings form natural geometric structures in vector space. The APD (Adaptive Parameter Discovery) algorithm automatically discovers cluster boundaries without predefined parameters using Torque-based MST analysis.

Works on any high-dimensional data (text, network, telemetry, audio)
Discovers natural clusters through mathematical rigor, not heuristics
No k-means style cluster count parameters required
Cosine distance avoids "curse of dimensionality" in high dimensions
Data flows from any source type into high-dimensional geometric space where Triaxis AI detects anomalies
See It In Action
Docker Appliance

Deploy Anywherein 5 Minutes

Single Docker container. Arrow Flight for high-performance ingestion. Deploy on cloud, on-premise, or edge devices.

Deployment Options

Enterprise Features

Hardware-Locked Licensing
RSA-signed JWT with fingerprinting
Air-Gapped Deployment
Zero external dependencies
Arrow Flight Protocol
gRPC-based high-performance ingestion

System Requirements (Minimum)

CPU
2+ vCPUs
Memory
4GB RAM
Storage
10GB
GPU
Not required
Request Trial License