See how the agent detects and fixes 38,000 anomalies in a product dataset, deduplicates a 180k-contact CRM, or responds to a data contract violation.
The Data Quality Agent is a Claude agent that monitors your data quality continuously: it detects anomalies, duplicates and inconsistencies, proposes fixes and checks that data contracts are respected. High-impact corrections go to a human for approval under LOOP™ governance, and every action is logged.
The agent works for data teams and for the business units that rely on the data. It replaces one-off clean-up campaigns with continuous monitoring, and each team stays accountable for its own data. It connects to your databases and data warehouse through the MCP protocol, without changing your stack.
Your production datasets: customer and CRM databases, product master data, the data warehouse and flows between applications. It also reads the rules that define correct data in your organisation, such as expected formats, mandatory fields, allowed values and the data contracts agreed between the teams that produce data and those that consume it.
It profiles each dataset, detects missing values, inconsistent formats, duplicates and rule breaches, then groups anomalies by type and severity. It proposes a fix for each one and, on the simple, reversible cases you have authorised, applies it directly. It also watches your pipelines so it can spot a contract breach as soon as it happens.
Fixes applied or proposed, each with its rationale and a way to roll it back. A quality report for each dataset that shows how it changes over time. When a data contract is breached, an alert to the team that owns the flow, with the offending sample and the rule it breaks.
Format fixes, value normalisation and merging obvious duplicates, on the datasets where you have allowed it. Every change is reversible and logged.
An uncertain merge of customer records, deleting records or a fix that touches shared master data. The agent prepares the change, and the data owner approves it.
A contract breach on a production pipeline or a large anomaly with no explanation. The agent fixes nothing: it stops the error from spreading if you have set that up, and alerts the owning team.
A supplier import introduces thousands of inconsistent values. The agent detects them, groups them by cause, fixes the standard cases and sends the rest to the person who manages the master data.
The same company appears under several names. The agent matches the records, merges those that leave no doubt and shows the uncertain matches to the sales team.
A production flow changes format without warning. The agent spots the gap on the first batch, identifies the rule that was broken and warns the producing team before dashboards are skewed.
Your production datasets: customer and CRM databases, product master data, the data warehouse and flows between applications. It also reads the rules that define correct data in your organisation, such as expected formats, mandatory fields, allowed values and the data contracts agreed between the teams that produce data and those that consume it.
It profiles each dataset, detects missing values, inconsistent formats, duplicates and rule breaches, then groups anomalies by type and severity. It proposes a fix for each one and, on the simple, reversible cases you have authorised, applies it directly. It also watches your pipelines so it can spot a contract breach as soon as it happens.
Fixes applied or proposed, each with its rationale and a way to roll it back. A quality report for each dataset that shows how it changes over time. When a data contract is breached, an alert to the team that owns the flow, with the offending sample and the rule it breaks.
Format fixes, value normalisation and merging obvious duplicates, on the datasets where you have allowed it. Every change is reversible and logged.
An uncertain merge of customer records, deleting records or a fix that touches shared master data. The agent prepares the change, and the data owner approves it.
A contract breach on a production pipeline or a large anomaly with no explanation. The agent fixes nothing: it stops the error from spreading if you have set that up, and alerts the owning team.
Deployed on enterprise data platforms, the Data Quality Agent detects and corrects anomalies autonomously.
30 minutes to validate feasibility on your processes. No slides, no pitch — a concrete conversation.