Embed World-Class Anomaly Detection Into Your Product
Triaxis AI Core Engine is designed to enhance, augment, or replace anomaly detection in your existing products. Send data via Arrow Flight, receive JSON results. Improve speed, accuracy, and reduce GPU costs.
Simple API Integration
Send your data, get anomaly detection results. Triaxis AI Core Engine integrates into your existing pipeline via Arrow Flight protocol.
Your Data
You provide
- Embedding vectors (any dimension)
- OR raw features with column names
- Streamed via Arrow Flight
Triaxis AI Engine
39K-259K samples/sec
JSON Results
You receive
- Anomalous feature identification
- Z-scores with baseline statistics
- Feature-level explainability
Actual Engine Output
{ "point_id": 9, "anomalous_features": [ { "feature_index": 22, "feature_name": "count", "z_score": 9.11, "value": 505.0, "baseline_mean": 22.05, "baseline_std": 53.00 }, { "feature_index": 23, "feature_name": "srv_count", "z_score": 8.21, "value": 505.0, "baseline_mean": 26.86, "baseline_std": 58.26 }, { "feature_index": 35, "feature_name": "dst_host_same_src_port_rate", "z_score": 3.41, "value": 1.0, "baseline_mean": 0.12, "baseline_std": 0.26 } ]}Actual output from Triaxis AI Core Engine showing feature-level anomaly explanations with z-scores and baseline statistics
Benchmark Validation Results
Real results from public benchmark datasets. These metrics demonstrate what Triaxis AI can bring to your product when you license the engine.

The ROI Case for Triaxis AI
Triaxis AI Core Engine is an investment that pays back in speed, accuracy, and infrastructure savings. Here is how licensing delivers value.
Speed ROI
Replace slow ML pipelines with real-time detection. Process 39K-259K samples per second on standard CPU hardware.
- Real-time alerting instead of batch processing
- Handle 10x more data with same infrastructure
- Reduce detection latency from seconds to milliseconds
Accuracy ROI
Match or exceed deep learning accuracy without training data, labeled examples, or hyperparameter tuning.
- Eliminate false positive fatigue
- Catch more true anomalies
- No model retraining required
Infrastructure ROI
Run entirely on CPU. Eliminate GPU infrastructure costs while maintaining enterprise-scale throughput.
- No GPU infrastructure required
- Lower cloud compute bills
- Deploy on edge devices
Integration ROI
Zero configuration means zero integration headaches. No ML expertise required to deploy and maintain.
- No data scientists needed for maintenance
- No hyperparameter tuning cycles
- Works across all data types
How Product Teams Use Triaxis AI
Enhance Existing Detection
Add Triaxis AI as an additional detection layer alongside your current solution for improved coverage.
Best for: Teams wanting to reduce false negatives
Augment Current Pipeline
Use Triaxis AI to pre-filter or post-validate results from existing models for higher precision.
Best for: Teams wanting to reduce false positives
Replace Legacy Systems
Fully replace outdated anomaly detection with modern geometric detection for maximum ROI.
Best for: Teams ready for infrastructure modernization
Triaxis AI is a Licensable Engine, Not a Standalone Product
We do not compete with your existing monitoring or analytics solutions. Triaxis AI Core Engine is designed to be embedded into your product, enhancing your anomaly detection capabilities with breakthrough speed and accuracy.
Ready to License Triaxis AI?
Start with a trial license to evaluate Triaxis AI Core Engine in your environment. Our team will help you integrate and benchmark against your current solution.
Trial License Includes
- Full access to Triaxis AI Core Engine
- Arrow Flight integration support
- Technical documentation
- Engineering consultation call
- Benchmark reproduction scripts
- Sample integration code
Contact Our Team
Tell us about your use case and we will set up your trial license
Request Trial Licenseinfo@triaxisai.com
Ideal For
Product Teams
Building anomaly detection into your software product
Platform Engineers
Modernizing legacy detection infrastructure
ML Teams
Looking to reduce GPU costs and training cycles