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Find metadata debt, approve one safe repair, and prove the result in DataHub.
A second AI agent that reads your DataHub lineage graph and has to approve every schema change before it ships — catching what your first agent structurally cannot.
Ratchet walks git history to find security regressions others miss — links every one to its cause, checks if it's still live today, and writes the evidence straight into DataHub's graph.
An AI agent that turns data goals into contextual investigation paths — powered by DataHub.
AI-powered dbt agent that reads DataHub metadata before writing SQL — so your models compile, test, and ship on the first try. Privacy-first. Closed-loop. Production-ready.
AI Suggests. Teachers Decide.
Finds documentation in your DataHub that stopped being true, proves it from DataHub's own change log, and fixes it behind a hash-verified human gate. 77 datasets scanned, zero false verdicts.
4 stale alerts across two platforms collapse to 1 table to fix — and the datasets nobody declared an SLA for become visibly UNMEASURED in the DataHub UI, instead of silently absent from the report.
When DataHub says a column exists but PostgreSQL says it doesn't, Sidq blocks the agent — and writes the proof back through DataHub's official MCP tools.
A swarm of 300+ AI agents stress-tests your MCP server with realistic and adversarial users, then scores every failure, ranks it by severity, and files the real bugs automatically.
Schema changes break every downstream dashboard. Koza solve this problem by interacting with the user and Data-hub through three interfaces e.g stream-lit, Slack bot and GitHub actions
Metadata-aware SQL change review grounded in DataHub schema, lineage, and usage—before breaking changes reach production.
A DataHub grounded production ML safety agent that tests & verdict whether a proposed upstream data change will silently damage downstream ML system before that change reaches production.
An agent that walks DataHub's ML lineage graph to catch silent model failures - and tells you, separately, what it couldn't check at all.
An autonomous AI agent for DataHub that monitors metadata, detects governance and lineage issues, explains downstream impact, and recommends or performs corrective actions.
We consume food daily, why not treat data the same? Nutri unifies freshness, health, and compliance into standardized Nutrition Labels with instant visibility and AI-powered, automated remediation.
Your PII tags stop at the source table. TagFlow doesn't.
A read-only DataHub MCP safety layer that resolves ownership, PII fields, and downstream lineage before an AI agent changes production data.
A personal context infrastructure that restores project continuity, traces decisions, and governs agent actions through human confirmation and verified re-reads.
Catch hidden ML pipeline changes before they break production.
Ant WMS, an AI-powered warehouse management system built to solve vendor data chaos, reactive slotting, and siloed reasoning.
In the NYC taxi sample, the metadata is invisible.All tables are is ingested together. The only way to catch it is to compare the real data timestamps inside each table. This sentinel does that.
it is basically the summary page/report for any road, bridge or transits project that TX dot is planning designing or building in Texas
Sentinel is an autonomous incident response agent for DataHub. It investigates, coordinates safe response, and writes verified knowledge back to the DataHub catalog. Every incident learns.
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