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A multi-agent AI team that reads DataHub over MCP, finds governance risks (failing checks, untagged PII, blast radius), writes approval-gated fixes back, and verifies them.
Graph-linked Decision Provenance for DataHub — replayable, reusable, and self-invalidating reasoning behind high-stakes data changes.
An evidence-backed agent that stops breaking warehouse changes before merge.
Turn DataHub failures into grounded, reviewable repairs with sealed approval and verified write-back.
AI agents trace wrongful claim denials to their root cause through DataHub's lineage graph — then fix them.
obsel tracks a right-to-erasure request across systems it cannot see: each asset reached stays unattested until someone who can look signs for it. The same lineage flags stale agent work.
Compile the forget request—don't guess it. DataHub-grounded remediation planner: lineage → reviewable plan → sandbox SQL → catalog writeback.
Executable data-rights revocation with DataHub. Revoke one upstream right; contain and verify every affected descendant.
File locks can't see a semantic collision. Graph Traffic Control gives autonomous data agents a DataHub-powered two-phase commit that can.
Break data safely before bad data breaks production.
Trace one deletion through the DataHub graph. Execute every consequence. Prove the result.
A DataHub-powered recovery compiler that turns live lineage into an approved, dependency-correct, executable recovery program.
AI Financial Operating System for the next generation of personal finance.
Catches silent ML failures — target leakage, stale upstream data — before they hurt you, by reasoning over DataHub's lineage graph. Discovers risk itself, writes fixes back as real PRs.
When a quality model fails silently, DataHub lineage finds the exact products at risk — including the shipped LOT a timestamp filter misses.
An autonomous agent that investigates your catalog's "cold cases" — undocumented, unowned tables — by cross-referencing schemas, lineage & real SQL queries, then writes the knowledge into the graph.
Cinematic Context Platform: AI agent giving video pipelines full DataHub context (lineage, MCP server, autonomous orchestration)
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.
Takes a job description + your resume, autonomously identifies skill gaps, rewrites resume bullets, generates tailored interview questions (like your NutriPlan mock-interview prep),
A read-only DataHub MCP safety layer that resolves ownership, PII fields, and downstream lineage before an AI agent changes production data.
Governs a clinical Snowflake+Metabase stack: PHI tags, dashboard certification, and pre-change impact alerts, all proposed via DataHub -- never auto-committed. Built on AWS Bedrock AgentCore.
Metadata-aware SQL change review grounded in DataHub schema, lineage, and usage—before breaking changes reach production.
Writes dbt models from your real DataHub catalog, then proves the SQL is correct before you see it.
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.
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