Your data's first line of defense.
AI-powered data reliability monitoring that detects anomalies, ensures quality, and protects your data before issues cascade.
SELECT DISTINCT * FROM transactions WHERE timestamp > NOW() - INTERVAL '1 hour'Real-time monitoring of data patterns with intelligent anomaly detection and alerting.
Suggested fixes and automated responses to common data quality issues.
Confidence metrics for every dataset, updated continuously as data flows.
Continuous monitoring and intelligent anomaly detection across your entire data pipeline
Connects to your data pipelines and establishes baseline patterns for volume, schema, distribution, and latency metrics.
AI models continuously compare incoming data against baselines, detecting statistical anomalies and pattern deviations in real-time.
Automatically gates suspicious data, alerts teams with actionable context, and suggests remediation strategies.
Advanced AI reasoning that understands your data's behavior patterns and context
Builds dynamic baselines that adapt to seasonal patterns, business cycles, and gradual data evolution
Sophisticated scoring that separates true anomalies from expected variance and noise
Traces anomalies back to root causes across complex data lineage and dependencies
Learns from human feedback and adjusts sensitivity based on operational outcomes
Distributed monitoring system with ML-powered anomaly detection and automated quality assurance
Multi-Source Collection
ML-Powered Detection
Automated Actions
Horizontally scalable with automatic sharding and load balancing across clusters
Multiple specialized models for different anomaly types with continuous learning
Auto-discovery of schemas and relationships with intelligent baseline generation
Built on Data Reliability
Each of these is Data Reliability pointed at one kind of problem — same core, same audit trail, same deployment — tuned for a domain. They are in development with design partners and are not sold separately.
Reroutes before the disruption reaches you.
Watches supplier signals and reroutes to a backup supplier automatically, within limits you set.
Catches what static rules miss.
A finance-tuned build on Data Reliability for synthetic identities, deepfakes, and agent-driven scams.
All the signal, none of the sensitive data.
Generates statistically faithful synthetic data so teams can build and test without the original.
Traceable defense at machine speed.
A SOC agent that correlates signals across your tools and contains threats within approved bounds.
Catches what a six-month review cycle misses.
Data Reliability's anomaly detection applied to clinical trial and research data in real time.
From predicted failure to scheduled fix, on its own.
Predicts factory-floor failures from sensor data and opens the work order automatically.