Your AI is only as good as the data beneath it.
Measured is the data quality foundation for everything you build with AI. It continuously validates the data going into your models and the results coming out: the quality layer your models, agents, and RAG pipelines run on.
Your data is ready for AI
Measured checked 10,200 records, caught 33 with negative values, and opened an incident, before any of it reached your model.
Connects to your whole stack
Measure data wherever it lives
Plus lakehouse, files, and more. Need one we don't list? Ask us.
AI doesn't fail loudly. It fails quietly, on bad data.
A null where there shouldn't be one. A silent schema drift. A duplicate that skews a feature. None of it throws an error; it just degrades every model, agent, and answer downstream. Garbage in isn't just garbage out anymore: it's hallucinations, bad decisions, and eroded trust at scale.
Bad inputs poison fine-tunes and retrieval, confidently and invisibly.
Distributions shift under your models with no alarm until accuracy tanks.
Generated records flow downstream unchecked, compounding errors.
One quality layer for everything your AI touches.
Quality in
Validate every input your models depend on: training sets, feature tables, RAG sources. Catch nulls, drift, schema breaks, and referential gaps before they reach the model.
Quality out
Treat AI results as first-class data assets. Validate generated records, scores, and the pipelines downstream of your models so errors never compound silently.
Your LLMs, plugged in
A native MCP server lets your own LLMs and agents query Measured, author rules, and investigate failures in plain language. AI that manages data quality for AI.
AI-authored rules
Profile any asset and Measured drafts the right checks: completeness, accuracy, uniqueness, ranges, consistency, structure.
Anomaly detection
Statistical and LLM-driven drift detection on volume, schema, and distributions, with plain-English explanations.
MCP server
Point Claude, your copilots, or in-house agents at Measured. They read quality, write rules, and triage failures over MCP.
Ask AI, in context
A contextual assistant that knows your org, workspace, and team, and links to the docs so nobody has to read them.
Bring your own LLM
OpenAI, Anthropic, Ollama, or any custom endpoint. Run it in the cloud or fully on-prem via the Privacy Agent.
Explainable failures
Every failed check comes with an AI root-cause summary and a suggested fix, not just a red X.
From raw table to trusted AI input, in five steps.
Connect
Point Measured at your warehouse, lakehouse, or database. Credentials can stay on your network via the Privacy Agent.
Profile
Auto-profile assets for stats, health, and structure: the raw signal the AI uses to understand your data.
Let AI draft rules
Accept AI-suggested checks on the asset, or write your own. Group them into cross-asset rule groups.
Validate
Run on demand, on a schedule, or from CI/CD and your agents via the API and MCP. Every run scores asset health.
Act
Route failures to alerts and incidents, sync results to your catalog, and feed clean signal back to your AI.
Runs the engine your data team already trusts.
Measured runs seamlessly on Great Expectations Core, the open-source data validation engine. Your existing checks work as-is, no rewrite, no lock-in, now supercharged with AI authoring, scheduling, RBAC, lineage, and observability.
- 100+ open-source check types, plus custom SQL
- API-compatible: point existing pipelines at Measured with one environment variable
- No proprietary rule format to get trapped in
# one environment variable, no code changes
export GX_CLOUD_BASE_URL=https://app.measured.cloud
export GX_CLOUD_ACCESS_TOKEN=dqk_•••••••••
# your existing pipeline just works
context = gx.get_context(mode="cloud")
context.checkpoints.get("portfolio-quality").run()Infrastructure you can put under production AI.
Privacy Agent
Run validation on-prem, between your data sources and the cloud. Data, credentials, and results never leave your network.
12+ connectors
Snowflake, BigQuery, Databricks, Redshift, Postgres, Oracle, SAP HANA, ClickHouse, Trino, MS SQL, MySQL, lakehouse.
Organization & teams
Six-role RBAC, SSO via Okta/Entra/Google, restricted PII/PHI/PCI assets, and full tenant isolation.
Alerts & incidents
Email, Slack, Teams, PagerDuty, Opsgenie, ServiceNow, Jira, webhook, plus a built-in incident lifecycle.
Catalog integrations
First-party push to DataHub, Atlan, Alation, and Data.world so quality shows up where people already look.
API-first
Every action is an API call. Automate from CI/CD, orchestrators, and your own agents, and pull metrics into PowerBI or Tableau.
Start free. Scale per seat.
For small teams getting started
- 1 user, 1 workspace
- 2 data sources, up to 25 rules
- AI rule suggestions
- Manual & scheduled validation
The full AI quality platform for your team
- Unlimited data sources & rules
- Full AI toolkit + MCP server
- Unlimited connectors
- Rule groups, alerts & incidents
- Organization & teams
Run it as production infrastructure
- Everything in Team
- Self-host & Privacy Agent
- On-prem LLM, SSO/SAML, RBAC
- Audit, SLA & dedicated support