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Aegis uses DataHub’s context graph to detect data risks, trace downstream impact, and keep AI-driven changes safe, compliant, and explainable.
Know what got into your model. Ariadne walks DataHub lineage to name the column that reached a deployed model, then files the finding back into the catalog as an incident.
Autonomous anomaly detection agent that monitors data pipelines, queries DataHub MCP Server for lineage context, identifies root causes, and generates actionable incident reports.
A self-healing loop for DataHub: when an AI agent fails, GraphMedic diagnoses the metadata gap, drafts a fix, and proves it worked
DataHub knows your owners, PII tags and freshness. It has no opinion. mlgate is that opinion: it gates ML releases on lineage and writes the verdict back.
GraphFixture catches silent SQL data loss by generating DataHub-aware fixtures, verifying business contracts, and writing replayable proof back to the catalog.
Relay gives every AI assistant lineage-aware decision memory, catching conflicting changes before they ship.
Which revenue number is right? MetricCourt puts every conflicting metric definition on trial, writes one canonical truth back to DataHub, and lets DataHub's own Analytics Agent answer from it.
DataHub X OpenTelemetry. A green AI run can still be wrong. DataHub explains the data. Replay checks the answer. OpenTelemetry proves what happened. Themis decides if the release can move.
DataHub knows where your ML features came from. UnderWrite makes CI act on that knowledge, blocking unsafe ML deployments before they ship.
ADIP is an autonomous 8-agent ecosystem that converts software code and UI exploration into interactive 3D video demos and Playwright QA suites
The ML lineage agent that just can't stop staring.
Retirement Conductor uses DataHub to find every downstream consumer, migrate and validate changes, then delete a legacy field; or stop when fresh evidence changes.
The on-call agent that catches silent ML failures your monitoring can't by walking DataHub lineage to the root cause and writing every finding back to the catalog.
Natural language search over your DataHub catalog using sentence embeddings — finds the right dataset by meaning, not just keyword matches.
DataHubX is a governance layer for AI agents acting on DataHub. Every change is approved against live data and re-verified at execution — if the data changed since approval, the action is blocked.
AI agents buy datasets with micropayments and every payment auto-splits as royalties up the DataHub lineage graph. Your catalog already knows who built the data. Now it can pay them.
LineageFix reviews critical database schema changes before they reach production
Change-Intelligence Layer for DataHub >> intercepts schema changes, computes risk score, generates remediation artifacts, and writes impact analysis back onto the DataHub metadata graph.
ChangeSafe by Luca uses DataHub’s metadata graph to find schema-change consumers, generate tested migrations, package them for Git review, and record decisions back in DataHub.
A DataHub-grounded agent that finds privacy risks hidden across lineage, proves them without exposing raw rows, and generates a review-ready dbt remediation PR.
An internal benchmark that tests whether AI agents are actually ready for your company.
Govern AI agents before they change production metadata. DataHub context drives deterministic risk, approval-bound one-use permits, verified mutations, replay protection, and searchable receipts.
Every affected model. Traced. Resolved.
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