From detection to decision to governed action...
“Data teams don't need another dashboard. They need to understand what happened, why it happened, what action to take - and how to execute that action safely under human governance.” - Matthew Thompson Co-founder & CEO
Our Services
AI-Powered Data Quality Intelligence.
The Problem:
Enterprise data teams identify thousands of data quality issues across customer, supplier, product, provider, patient, and other critical data domains. Traditional profiling tools can identify exceptions, but teams are still left determining what the findings mean and what action should be taken.
The Challenge
Data stewards must translate technical findings into business impact, remediation decisions, and actionable next steps - often manually and across multiple systems.
How AI Data Steward Copilot Helps
AI Data Steward Copilot combines deterministic Data Quality findings with AI Decision Intelligence to explain the issue, identify affected records, assess business impact, and recommend an appropriate remediation path.
Outcome
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Faster identification and resolution of data quality issues
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Reduced manual analysis
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Clearer business impact and remediation guidance
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Stronger Data Quality governance
Governed AI Remediation & Execution.
The Problem:
Identifying a data issue is only part of the problem. Data teams still need to determine the correct remediation, validate it, obtain approval, execute it safely, and document what happened.
The Challenge
Moving from an AI recommendation to an executable change introduces operational and governance risk - particularly when enterprise data is involved.
How AI Data Steward Copilot Helps
The Copilot can generate an exact remediation artifact, route it through Steward and Technical Approval, validate it against an authorized Enterprise Connection, and execute approved remediation in controlled DEV/STG environments.
Production execution remains intentionally protected.
Outcome
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AI-generated, deterministic remediation
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Human approval before execution
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Controlled DEV/STG execution
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Reduced manual remediation effort
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Complete execution audit trail
AI proposes. Humans approve. Copilot executes safely. Every action is auditable.
Explainable MDM & Entity Resolution Decisions.
This replaces “Resolving Confusing Match Decisions.”
The Problem:
Traditional MDM platforms often provide match scores and rule results without giving stewards a clear explanation of the complete decision.
The Challenge
Stewards must determine why records matched, whether a merge is safe, which signals drove the decision, and where risk exists.
How AI Data Steward Copilot Helps
The Copilot analyzes match evidence across identity attributes, source trust, domain identifiers, policy thresholds, and other resolution signals to provide an explainable AI decision with confidence, risk, evidence, and a recommended action.
Decisions can include AUTO_MERGE, APPROVE_MERGE, REVIEW_REQUIRED, and BLOCK_MERGE.
Outcome
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Faster match and merge decisions
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Reduced investigation time
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Greater confidence in entity resolution
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More consistent stewardship decisions
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Improved visibility into match risk
AI-Assisted Data Stewardship.
The Problem:
Data stewards spend significant time moving between rules, scores, source records, exceptions, and dashboards simply to understand what happened.
The Challenge
As data volume increases, manual investigation becomes a major operational bottleneck.
How AI Data Steward Copilot Helps
The Copilot consolidates decision evidence and answers the questions stewards actually need:
What happened? Why did it happen? How confident are we? What is the risk? What should we do next?
Stewards can then accept the AI recommendation or override it with their own governed decision.
Outcome
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Reduced investigation time
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Faster stewardship decisions
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Consistent decision workflows
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Human oversight of AI recommendations
Human Feedback & AI Decision Learning
Your existing Learning from Steward Feedback section is good, but the new product can say more.
The Problem:
Human decisions contain valuable governance intelligence, but most platforms record the final disposition without learning from why stewards agreed or disagreed.
The Challenge
Organizations cannot easily identify where AI recommendations, matching rules, confidence thresholds, or governance policies repeatedly conflict with human expertise.
How AI Data Steward Copilot Helps
The Copilot captures AI recommendations, steward decisions, overrides, reasons, confidence levels, and decision history. These signals can reveal agreement patterns, frequently overridden rules, false-positive/false-negative patterns, and areas requiring policy refinement.
Outcome
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Measurable AI/human agreement
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Visibility into override patterns
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Better policy and threshold refinement
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Continuous improvement of decision quality
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Stronger human-in-the-loop AI governance
Data Governance & Policy Intelligence.
The Problem:
Governance policies are often documented separately from the operational systems where actual data decisions occur.
The Challenge
Organizations struggle to translate governance policies into repeatable controls over AI decisions, stewardship, remediation, and execution.
How AI Data Steward Copilot Helps
The Copilot operationalizes governance through policy thresholds, risk controls, approval requirements, certification readiness, execution permissions, and environment-aware controls.
Governance becomes part of the decision workflow - not simply documentation surrounding it.
Outcome
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Operationalized governance policies
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Consistent approval controls
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Greater visibility into governance risk
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Improved certification readiness
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Stronger accountability for data decisions
Governed Enterprise Data Connections
The Problem:
AI recommendations become significantly more valuable when they can safely interact with enterprise data platforms - but unrestricted AI access creates unacceptable risk.
How AI Data Steward Copilot Helps
Enterprise Connections establish a governed path between Copilot and enterprise data platforms with organization, environment, authentication, and capability controls.
Connections can support controlled activities such as read, profile, AI analysis, validation, and approved remediation execution, while production environments remain protected from autonomous execution.
Outcome
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Governed connectivity to enterprise platforms
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Environment-aware execution controls
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Clear separation between analysis and execution
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Safer adoption of AI in enterprise data operations