Based on Gartner Framework

Bad Data In,
Bad AI Out

You're paying to move, store, and process garbage. What if you fixed it before it left the source?

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The three pillars

The Three Pillars of AI-Ready Data

Gartner's framework defines three continuous processes. Expanso implements all three at the source.

Alignment

Add context, meaning, and lineage at creation. Stop reverse-engineering what fields mean.

Learn About Alignment

Qualification

Validate schemas, detect anomalies, filter bad data before it moves. Invalid records go to dead-letter queues.

Learn About Qualification

Governance

Apply customer-defined handling, routing, and residency policies at origination.

Learn About Governance

Why at source

Why Process Data at the Source?

By the time data reaches your warehouse, you've already paid to move and store it. If it's bad, you pay again to clean it.

Catch Problems Immediately

Quality issues, missing context, compliance violations - fixed before data moves.

Problems caught at origin

Stop Paying for Garbage

Filter noise, duplicates, invalid data before it hits Snowflake, Databricks, or Splunk.

Lower platform costs

Policy at the Source

Evaluate customer-defined handling and residency requirements before data moves.

Policy-driven routing

Real-Time Ready

Clean, contextualized data available immediately. No overnight batch jobs.

Sub-second latency

Use cases

See It In Action

U.S. Navy

Sensor data across distributed naval assets with strict compliance.

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Smart City Infrastructure

Real-time data from thousands of IoT sensors and cameras.

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Governance Evaluation

Test handling and residency policies against your regulatory requirements.

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Platform Cost Optimization

Reduce Splunk and warehouse costs by filtering at the edge.

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Ready to start?

Ready to Stop Processing Garbage?

Alignment, Qualification, Governance, at the source where data is created.

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