Atlas Intelligence
Everyone now says "governed semantic layer".
Warehouse-native NL analytics, AI catalogs, text-to-SQL copilots — the marketing reads almost identically to Atlas. So don't take the claim. Test it. Four questions cut past the language collision to what a tool actually does.
Four tests that cut past the claim
Open a test to see how Atlas answers it.
Test 1
Does it scope the decision before answering, or race to SQL?
How Atlas answers
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Atlas treats the literal question as a starting point, not a spec. It scopes the decision behind the question, surfaces the assumptions that change the result, and routes "I'm not sure" to whoever holds the answer — before generating SQL. In analytics a fast, confident, wrong answer is worse than none; tools that race to a query optimize for exactly that failure.
Test 2
Does its "ontology" compile into your dbt with drift detection — or is it just query-engine config?
How Atlas answers
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A warehouse or catalog "ontology" is configuration for a query engine, or a passive glossary beside the warehouse. The Definition of Record is agreed, versioned business intent that compiles into the customer's dbt and semantic layer, with bidirectional drift detection. A glossary describes; the Definition of Record governs and stays bound to the implementation.
Test 3
Does it build the missing pipeline through governance, or only answer within prepared data?
How Atlas answers
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When the data isn't prepared, Atlas proposes governed pipelines, models and PRs across the full lifecycle — ingest → model → orchestrate → test → review → deploy — through the Task Loop into GitHub / Jira. Warehouse-native NL tools answer only within already-prepared data; when new data is needed, their workflow breaks.
Test 4
Is it portable over your stack, or a platform you must adopt?
How Atlas answers
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Warehouse-native NL is bound to one vendor's warehouse. Atlas runs over the customer's existing dbt / warehouse / Git / orchestrator via MCP, and never traps definitions in one vendor. The stack-locked players structurally can't take this position — they depend on lock-in; Atlas wins by working everywhere.
The same words. Different answers.
One row per test, one column per category.
| The test | Atlas | Warehouse-native NL | AI catalogs | Generic text-to-SQL |
|---|---|---|---|---|
| Test 1. Understanding-first |
Scopes the decision first
|
Races NL → SQL
|
Describes, doesn't answer
|
Returns the literal request
|
| Test 2. Definition of Record ≠ ontology |
Compiles into dbt + drift detection
|
Query-engine config only
|
Passive glossary, no binding
|
No definitions at all
|
| Test 3. Builds the missing infrastructure |
Proposes governed pipelines & PRs
|
Only within prepared data
|
No build path
|
No governance or lineage
|
| Test 4. Portable, not stack-locked |
Runs over your stack via MCP
|
Vendor-locked
|
Separate platform to adopt
|
Point tool, no lifecycle
|
Team convergence, not shadow metrics
Atlas converges a team's intent into one Definition of Record and flags when two conversations drift toward divergent definitions of the same thing — prevention at creation, not cleanup after.
Intent IP compounds on your side
Every agreed definition becomes customer-owned organizational memory. The fifth question of a kind starts most of the way done. A tool that only answers the literal request bills full price every time.
Run the tests on us — and on them.
AI moves fast. Governance keeps it trustworthy. Atlas brings both to the stack you already have.