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An autonomous, context-driven AI agent engineered to automate heavy corporate compliance and contract risk assessment by isolating hidden liabilities and flagging high-risk operational terms.
An AI agent that scans entire MongoDB collections to discover hidden schema blind spots DataHub misses — then writes enriched metadata back to the catalog.
Metadata-Aware Code Generation & Development challenge
Fail-closed governance for DataHub agents: read metadata, verify evidence, generate tamper-evident Proof Packs, and allow write-back only when deterministic checks pass
A DataHub-aware CLI that scans ML lineage for production risks: schema drift, PII exposure, stale data, missing ownership, feature leakage, performance regression, and deployment config drift.
Instaboard lets workers record themselves doing day to day tasks, turns it into a step-by-step guide for new hires, and warns you when the guide goes out of date.
SourceFit is a DataHub-powered agent that measures a vendor dataset’s real lift, blocks usage-rights violations, generates the integration, and writes the decision back to your data catalog.
AI-powered regulatory impact analysis using DataHub lineage and metadata
An AI incident crew whose postmortems are written into DataHub, so the next investigation starts from what the last one learned.
Context circuit-breaker for DataHub: audit, selective quarantine, fix artifacts, and MBOM (metadata bill of materials) attestation so agents inherit safer catalog knowledge.
A clinically informed governance agent that finds healthcare data-contract violations in DataHub lineage, drafts a patch that actually applies, and writes its findings back into the graph
Sherlock turns DataHub metadata into disciplined investigations, challenging hypotheses, exposing missing evidence, and guiding teams toward the next best action.
AI-powered tool that tells you exactly what dashboards, pipelines, and tables will break before you change a database schema — using DataHub's lineage graph to prevent data disasters.
The organizational memory engine for DataHub: an agent that investigates data incidents, proves the root cause with evidence, and turns every resolution into reusable knowledge.
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.
Data health monitoring tools analysis engine with outputs people actually consume.
Intercepts AI agent actions before execution, enriches them with DataHub metadata over MCP, and returns deterministic approve, deny or human review verdicts with an EU AI Act ready audit trail.
An autonomous data operations center using LangGraph multi-agent AI and DataHub to trace root cause lineage, auto-heal broken pipeline schema configs, and alert dataset owners in real-time.
From lineage impact to verified change readiness.
AI-native women's safety ops — ShieldHer PWA + Splunk MCP Server + 3-agent pipeline (Triage→Response→Audit)
Every DataHub lineage node becomes a live data profile: broken columns light up the models & dashboards they poison, priced in €/day — and the rescue is filed right back into DataHub.
A stack trace for model decay. Give it a degraded model and a vague complaint, and it walks DataHub's ML lineage to the column that broke it, prices the damage in dollars, and opens the PR.
Turns DataHub-shaped metadata into governed, bounded, deterministic work packets for human review.
A camera feeds bad training data for three weeks, which robots learned from it? Recall walks DataHub lineage forward, stops exactly those machines and writes the recall into the catalog.
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