AI Classification
Context-Blind Systems Misclassify Sensitive Data
Without understanding data context, legacy tools cannot differentiate between real and synthetic data leading to inaccurate classification and hidden risk.
Unstructured Data Lacks Machine-Readable Patterns
High-value data in documents and files doesn’t follow consistent formats, making traditional pattern-based detection ineffective.
Rules Fail as Data Evolves
As data formats shift, static classification rules degrade leaving gaps that expose sensitive information.
Contextual Detection
Matters looks at the surrounding metadata and text to determine if a string of numbers is a part number or a social security number.
Semantic Labeling
The agent categorizes data by "intent" and "business type" (e.g., "Board Meeting Minutes") rather than just looking for trigger words.
Self-Refining Accuracy
As your data grows, Matters’ AI learns your specific industry language, constantly improving its precision without human retraining.
Continuous Tagging
Every new file is instantly scanned and tagged with a permanent, searchable risk-identity as it is created or moved.
Proven Track Record
Matters delivers 99% classification accuracy for unstructured data, outperforming legacy competitors by 3x in head-to-head pilots.
