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RecallOps uses DataHub context, PostgreSQL agentic memory, and validated AI investigation to turn data alerts into safe, human-approved next steps.
FTIR spectral agent retrieval kernel optimized for Arm64 — real cross-architecture benchmark, reproducible on GitHub Actions ARM runners.
Governance proxy for AI agents writing to DataHub
An AI-powered DataHub agent that detects metadata, lineage, governance, and data-quality issues, explains their business impact, and generates actionable SQL fixes.
Ripple AI: Autonomous Self-Healing Data Pipeline
An agent that finds the silent failures in your dbt warehouse — green build, green tests, wrong revenue — proves them empirically, and hardens against them.
Retirement Conductor uses DataHub to find every downstream consumer, migrate and validate changes, then delete a legacy field; or stop when fresh evidence changes.
AI agent that reads real DataHub metadata (schema, lineage, glossary) and generates runnable dbt models + Airflow DAGs, writing results back to DataHub lineage.
An agent that reads your DataHub catalog every day, writes what it found in four voices people actually read, and writes it back so the next person inherits it.
A column gets dropped, a serving model goes quietly wrong, nobody notices for weeks. Tether blocks that PR. When it misses one, it writes the missing lineage edge to DataHub, and stops missing it.
Turn a whiteboard photo into a validated dbt + Airflow PR — grounded in DataHub via the MCP Server, tested in DuckDB before merge, with column-level lineage written back to the catalog.
DataHub-grounded schema migration agent with deterministic policy, counterfactual dbt proof, immutable safety cases, and post-merge verification.
Two dashboards disagree about revenue. RIFT proves exactly why it rewrites the SQL, executes it, and measures what each clause is worth in dollars — then writes the answer back to DataHub.
An AI agent that detects data governance gaps, reasons over DataHub context and lineage, and writes approved fixes back to the catalog.
Govern every AI action. Trust nothing by default. Zero-trust governance layer for AI agents with policy, reputation-gated, delegation-scoped, and written to a tamper-evident audit chain.
A tested Windows Quickstart audit and documentation fix that replaces Unix-only commands, aligns Python guidance with the CLI, and adds native PowerShell setup examples.
A PR bot that reports the real downstream impact of a dbt schema change — dashboards, ML models, and owners — before you merge, then writes the verdict back into DataHub.
An agentic reliability control room using DataHub lineage, quality, and ownership context to stop healthcare data and ML pipeline issues before they impact clinical operations.
DataOps Command Center: 4 AI agents that read DataHub, do real work (enrich docs, generate code, monitor ML, auto-tag PII), and write results back. Chat, voice, live dashboard, mock-ready.
Generate trusted SQL. Detect schema drift. Repair broken queries automatically. DataHub Aegis keeps analytics reliable as enterprise data evolves.
An AI-powered data engineer that understands metadata, diagnoses data issues, and automates reliable data workflows.
The AI agent that reads DataHub lineage and blocks the schema change that would break production.
Protect the scientific decision, not just the data pipeline. SciGuard detects silent drift, proves exact impact with DataHub lineage, contains selectively, and ships a proof-carrying repair.
Your migration copilot
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