AI agents that watch your data estate continuously, so teams act on insight, not incident recovery.
ArqEye is an AI-native data observability platform that monitors the health, freshness, quality, and lineage of data assets continuously across pipelines, warehouses, and data products. It moves data incident management from reactive discovery to proactive prevention — detecting anomalies at ingestion and transformation, tracing failures to their origin, enforcing SLAs per data product, and routing incidents with full context assembled rather than requiring manual investigation from scratch.
Built for: Data engineering, analytics engineering, BI teams, CDOs, and data-intensive SaaS
Typically owned by: Head of Data Engineering and VP of Data Platform; Chief Data Officer and VP of Analytics; Head of Analytics Engineering and Director of Data Infrastructure; Platform Engineering leads at data-intensive SaaS companies.
Targets we engineer each deployment toward, measured against your baseline during rollout.
Data teams discover quality issues when a dashboard breaks, an ML model produces unexpected outputs, or a stakeholder notices something wrong. By then the failure has propagated downstream, the root cause is buried in hours of pipeline logs, and the impact scope is unknown until manually traced. Data SLAs exist on paper with no automated mechanism to monitor or enforce them.
ArqEye converts data incident management from reactive recovery into proactive monitoring. Issues are detected at the point of entry, not after reaching consumers. Root cause is surfaced by an agent rather than reconstructed manually from pipeline logs. Impact scope is known immediately through lineage mapping. SLAs are enforced continuously rather than reported on retroactively.
A reusable workflow spine, tuned to your data, systems, and controls — not a generic model wrapper.
Monitors volume, freshness, schema drift, null rates, and statistical distribution across tables and pipeline stages continuously — flagging deviations before they propagate to downstream consumers, dashboards, or ML pipelines.
Automatically maps and maintains the dependency graph of upstream sources and downstream consumers for every data asset. When a failure is detected, the full blast radius is known immediately.
An LLM agent traces a data quality failure back to its origin — source schema change, upstream load failure, transformation drift, or infrastructure event — and surfaces the root cause with evidence and remediation options.
Defines and continuously monitors freshness, completeness, and quality SLAs at the data product level — triggering alerts and automated fallback logic the moment a breach threshold is approached, not after it's missed.
Tracks schema changes across source systems and pipeline stages in real time. Unplanned changes fire an immediate alert with the change description and affected downstream assets, preventing silent data corruption.
A continuously updated health score per data asset across completeness, accuracy, consistency, timeliness, and validity — with trends visible before degradation causes incidents.
Every agent action carries the trigger, the reasoning, the inputs, and the outcome in an encrypted, persistent audit trail. No black boxes.
A collection agent layer connects to data warehouses, lakes, and streaming systems to capture metadata, statistics, and schema snapshots continuously. A detection agent runs anomaly models and threshold checks per asset and per pipeline stage.
A lineage agent maintains the dependency graph and evaluates blast radius on each detected anomaly. An RCA agent chains against the lineage and change history to trace root cause. All findings route to a monitoring dashboard and alert channels with full context, evidence, and suggested remediation attached.
Accelerators move fastest when the first release is narrow, measurable, and connected to the people who own the work.
Connect the data warehouse, primary pipelines, and orchestration tool; baseline volume, freshness, and schema patterns across priority assets.
Deploy anomaly detection and schema drift detection on production data flows; set SLA thresholds for priority data products; validate against recent incident history.
Enable lineage intelligence and root cause analysis; wire alerts to existing incident channels; begin SLA enforcement automation.
Expand coverage to the full data estate; activate health scoring dashboards; calibrate thresholds from observed true/false positive rates.
Continuous volume, freshness, and distribution monitoring across warehouses, lakes, and streaming systems.
Stop bad data from silently corrupting ML features and BI dashboards, with blast radius known in seconds.
Turn documented data SLAs into continuously enforced commitments with automated alerting and fallback logic.
ArqEye connects to your existing data infrastructure — warehouses, orchestration, streaming, catalogs, and alerting — without replacing it, adding a continuous monitoring and intelligence layer across any platform or cloud environment.
A two-week analysis of recent data incidents, detection lag times, root cause investigation hours, and current SLA compliance across priority data products. Delivers a maturity score, a gap analysis against observability best practice, and a prioritized ArqEye deployment roadmap.
Book itArqEye is an AI-native data observability accelerator. Agents continuously monitor the health, freshness, quality, and lineage of data assets across pipelines and warehouses — detecting anomalies at ingestion, tracing root causes automatically, and enforcing SLAs per data product.
ArqEye is the observability platform: estate-wide health, freshness, lineage, schema drift, and SLA enforcement. ArqDataQ is the quality remediation system: detecting and autonomously fixing data quality issues in pipelines. They share the governed agent spine and are often deployed together.
An LLM agent chains against the lineage graph and change history to trace a failure back to its origin — a source schema change, upstream load failure, transformation drift, or infrastructure event — and surfaces it with evidence and remediation options in seconds instead of hours of manual log analysis.
Warehouses including Snowflake, BigQuery, Redshift, and Databricks; orchestration via Airflow, dbt, Prefect, and Dagster; streaming via Kafka, Confluent, and Kinesis; catalogs like Alation, Collibra, and DataHub; and alerting through PagerDuty, OpsGenie, Slack, and Teams.