Trusted Data
for the AI Era

Observability, Quality, Governance, and Discovery in one control plane.

Built for engineers and business teams. Powered by AI agents.

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SCALE DATA & AI PRODUCTS RESPONSIBLY

The Data & AI Control Plane

Siloed tools fragment information and erode trust. Our unified control plane is built on a context engine, bringing metadata, lineage, logs, validations, and health signals together to power every workflow and every AI agent with reliable data.

Pipelines scale faster
than people

Thousands of data sets and infinite changes outgrow human capacity. You need AI to manage, monitor, validate, and triage data at scale, and reliable data is needed to power AI products.

AI for data reliability -> reliable data for AI

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AI agents are the
only way to keep up

Validate Data Quality

Triage and resolve issues

Enrich and Maintain Metadata

Analyze and Improve Test Coverage

Find and Understand the Data

Prevent breaking changes

Optimize Query Performance

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Built for engineers, business users, and AI

FOR ENGINEERS

Code-first
observability

Shift-left by managing tests, rules, and metadata in code. Observability should be managed like your pipeline: versioned, reviewed, and built with AI.

FOR BUSINESS USERS

AI-first data quality,
discovery & governance

Users can get answers on data assets, how they are used, whether they are reliable, who owns them, and can safely contribute validations and documentation to the codebase.

Code as the
source of truth

Bring tests, rules, and metadata into your codebase so you can prevent drifts and scale AI reliably.

dbt-Native
by design

The Elementary dbt package seamlessly integrates your tests and artifacts with your data warehouse.

INTEGRATIONS

Any pipeline.
Any data.
End-to-end.

Ensure reliability across ingestion, transformation, semantic layers, BI, and AI through a shared context engine that collects and applies metadata, lineage, tests, performance signals, and usage patterns across your stack.

Integrates with every part of your data stack
AI agents and tools operate with shared context
Full lineage awareness across the entire pipeline

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Teams doing data right

“Thanks to Elementary, we only get alerts on real production failures. No alert fatigue, just real issues.”

Aviv Netel

Data Engineering Team Lead

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"Each stakeholder can open the screen in the morning, get a quick overview of the data — and feel confident in its quality"

Shenhav Lavie

Director of Data Engineering

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"With Elementary, we’re testing, tagging, and producing a ton of high-quality metadata — everything we need to establish that golden standard of trust."

Cory Woytasik

Data Architecture / Governance Leader

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The tool your team will actually use

Incidents & alerts

Group related failures into clear, managed incidents and route context-aware alerts based on ownership and severity.

Automated pipeline monitoring

ML-based, out-of-the-box monitors that detect anomalies in freshness and volume before they reach downstream assets.

Data quality

Data quality checks that surface anomalies and inconsistencies, with built-in and custom tests built for both engineers and business users.

Column-level lineage

Understand your entire pipeline, from ingestion to BI, with column-level lineage that shows how each field is produced and what it impacts downstream.

Catalog

Ask about any asset and get definitions, ownership, tags, tests, usage, and health explained in a clear, conversational format.

Health scores

Get an overview of data health across domains, teams, and assets, with scores that measure all core data quality dimensions.

MCP Server

Expose Elementary’s context layer and agents through a standard MCP Server interface, making lineage, metadata, and data health available in any AI tool.

Performance & cost

Identify slow or expensive tests and models, understand their downstream impact, and surface optimizations that improve performance and reduce compute cost.