Atlas

The AI-ready layer. Turns your canonical data into something your agents can actually use.

AOn the roadmap

What Atlas does

Atlas turns the harmonized, canonical model into AI-ready structures: per-entity embedding and index plans, a semantic layer exposed as MCP and API resources for retrieval, and the metrics and aggregations your agents ask for, kept in sync as the data changes.

It's been elevated from a nice-to-have to a first-class delivery mode: MCP + graphRAG + RAG, populated straight from your trusted data. Today Atlas exists as a deterministic planner that emits the AI-ready descriptor; the full build is next on the roadmap.

The loaded canonical model.

Out

An AI-consumable surface (MCP, retrieval, metrics) your agents query.

Capabilities

What’s real under the hood

✓Per-entity embedding and vector-index plans

✓Semantic layer exposed as MCP + API resources

✓Retrieval surface (RAG / graphRAG) over trusted data

✓Metrics and aggregations for agent consumption

✓Kept in sync with the canonical model

How a run moves through Atlas

Plan

Produce the AI-ready descriptor for each entity.

Build

Stand up the embeddings, index and endpoints.

Sync

Keep the surface current as the data changes.

“Trusted data in, an AI-ready surface out, a first-class delivery mode.”

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Theo · Deliver

How it works

EEMBD

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