Your data model is the context. Your AI finally gets it.

Without context, AI guesses confidently and incorrectly. SqlDBM AI fixes that: your governed data model becomes trusted context for every person and agent you run.

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What you can do in SqlDBM AI Today

Live today and all grounded in the data model you already govern.

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01

Copilot

Draft data models from requirements, reverse engineer from documents, fill in descriptions and logical names, flag PII, and suggest improvements. It works only from your model and metadata, never your data, and you review everything before it lands.

02

Semantic Models

Author metrics, synonyms, and verified queries in a governed environment instead of hand-editing thousand-line YAML files. Import the semantic models you already have, refine them here, and forward-engineer them to your warehouse.

03

MCP

Your governed data model, served over MCP to whatever your organization runs: Claude, Gemini, Cortex. Analysts ask questions from the chat tool they already use. Engineers read table definitions without leaving the IDE. Agents ground answers in the approved model instead of guessing. Read access is live, authenticated, admin-visible, and revocable in one click.

04

AI-generated documentation

Descriptions, synonyms, and logical names generated in bulk, reviewed by your stewards, published to the business automatically.

05

Copilot skills

Encode your team’s standards, governance rules, and review workflows as reusable AI procedures instead of repeating the same prompts across every session. Any member of your organization can invoke them, and Copilot learns to apply the right skill automatically as it learns your intent.

06

Your keys, your boundary

Bring your own model. Your AI accounts, your credentials, everything inside your security perimeter. We touch the model and metadata, never your rows.

Coming soon

What comes next

From governed context to the data model of record

Data Model of Record

The approved version as the source of authority — latest-approved, not latest-edit.

Deep data model analysis

Multi-step reasoning across your whole data model for the heavy work: complex migrations, impact analysis, large-scale refactoring.

Supervised agents

Starting with a Governance Agent: it watches your data models for drift, checks every change against your data modeling standards, and reviews merges before they land. A person always holds the approval.

Data model memory

Your standards, decisions, and data modeling patterns carry across every session, so AI work builds on your governed data model instead of starting from zero.

Nothing lands in your data models unsupervised.