The ADE family
Modern analytics is fragmented: notebooks on one platform, semantic models on another, pipelines everywhere. The ADE family closes the loop with three verbs — understandwhat exists, operate it safely, and consume the certified result — all built to be driven by AI agents, not just read by humans.
ade-catalog
Understand — cross-platform analytics metadata catalog
Maps your entire analytics landscape — what exists and how it's connected. It extracts metadata across platforms, builds cross-platform lineage native tools don't provide, and exposes everything through a navigable web UI and an MCP server for AI agents.
ade-ops
Operate — operations framework for multi-platform analytics teams
Most data-platform tools hand you a sample and walk away. ade-ops asks what you have, then scaffolds the workflow to operate it — keeping notebooks, semantic models, and reports in sync across dev, cert, and prod from a single source of truth, with diff-before-push safety.
ade-lens
Consume — guided consumption for business end-users
A governed, cross-tool consumption layer over an existing BI estate. It finds and frames the right, certified report as native in-page objects, and takes business users to the certified answer — with the proof. It doesn't give you a plausible answer; it gives you the official one.
The YouTube series
The Agentic Data Engineer is a video series documenting the journey of building an AI-based agentic data engineer — from metadata extraction to autonomous pipeline navigation, from lineage tracking to code generation.
It's not a tutorial. It's a logbook — showing what works, what doesn't, and what happens when you give an AI agent real access to a data ecosystem.
The vision
Each analytics system has its own metadata, its own interface, its own language. ADE was born to close that gap — not with another dashboard, but with products an AI agent can drive: one that understands what exists across the stack, one that operates it safely, and one that serves the certified answer. Already in production with enterprise data teams.
The goal isn't to replace the data engineer. It's to give them a colleague that never sleeps, never forgets, and can traverse the entire stack in seconds.