About Alexis Networks

Alexis Networks delivers actionable predictive insights by discovering hidden trends and patterns.

ALEXIS is a single stack software offering including – Data Lake Processor, ML Data Pre-Processor, Data Anomaly Detection, AI Anomaly Detection (preventing model poisoning), App Exchange and Business Solutions. We recognize that customers today are having to cherry pick different vendors for these different technology needs, we are delighting our customers by taking ownership of their anomaly detection problem from start to finish.

• ALEXIS Data Lake Processor
• AI Anomaly Detection
• ALEXIS Data Processor
• ALEXIS App Exchange
• Data Anomaly Detection
• ALEXIS Lens (Dashboards)

ALEXIS DLP and ADM

ncDLP

• No code utility to create Data Lake from operational, transactional and data warehouses

ncDLP-S

• No code utility to create Data Lake from operational, transactional and data warehouses - on streaming data

ADM

• No code utility to process and analyze data in the data lake to create a feature identity matrix for analysis by ALEXIS AI / ML

ADM-NLP

• No code utility to process and analyze data in the data lake (including unstructured data) to create a feature identity matrix for analysis by ALEXIS AI/ML.

Data Anomaly Detection

ALEXIS finds Fraud, Omissions and Error in your application and enterprise data.

FRAUD

• Fraud is the most expensive type of Anomaly. Fraud typically accounts for 50% of the Anomalies. Alexis technology has been used for multiple years and has shown to customers that ignoring Fraud has both a one-time financial loss of revenue and impacts EBIDTA margins on a recurring basis.

OMISSION

• Omission is one the most common types of Anomaly. Omission typically accounts for 25% of the Anomalies. Alexis technology has been used for multiple years and has shown to customers that ignoring Omission creates significant corruption of data over time.

ERROR

• Error is the one of the most common types of Anomaly. Error typically accounts for 25% of the Anomalies. Alexis technology has been used for multiple years and has shown to customers that ignoring Errors creates systemic issues - process and technology - in the company.

AI Anomaly Detection

Data Poison

• Automatically detect when the deployed AI Model is subject to model poisoning resulting from data poisoning

Model Drift

• Automatically detect when the deployed AI Model is drifting from its initially deployed state

AI Model Explain

• Automatically provide insights into the deployed and operational AI Model to help understand causes of model poisoning or model drift

Feature Matrix

• Provides a detailed insight into the features in data causing the AI Anomaly Detection in the form of Target Variable Descriptors