Know if your data is ready for reliable signal extraction.
Composed AI audits database tables, event logs, and unstructured sources before they enter your signal pipeline, so teams can fix schema gaps, missing fields, and unreliable data early.
Seven scored checks for AI-ready data.
Schema compatibility
Check whether columns, fields, and formats are usable for extraction.
Field coverage
Measure whether required fields exist across records and sources.
Source grounding
Check if unstructured files are parsed, indexed, and correctly linked to database rows.
Null & missing values
Measure empty, corrupted, or null values across critical signal columns.
PII redaction
Verify automated removal of personal data before signal extraction.
Signal-to-noise ratio
Filter duplicate database logs and automated bot entries.
Timestamp consistency
Verify standard offset-aware formats to prevent chronological sync errors.
Choose your depth. Test before every launch.
Quick Schema Check
Scan up to 100 rows. Fast assessment of column names and basic null ratios.
Standard Source Audit
Scan up to 5,000 rows. Full assessment of schema, field depth, and data cleanliness.
Deep Pipeline Audit
Scan all connected tables. Deep compliance, PII redaction checks, and structural verification.
Configure customized schemas or rerun data readiness checks directly from your dashboard.
Fix issues, retest, and keep quality from drifting.
Connect your database sources or upload your unstructured files.
Run the AI Readiness Audit to scan schemas, check values, and score dimensions.
Review the scorecard report, update missing fields, and deploy signal extraction with confidence.