AI Client Dataset Validation Agent

How Cassidy automatesusing AI

Step 1: Trigger on dataset intake

The Workflow activates when a new client dataset arrives1whether uploaded to a secure landing zone, synced from cloud storage, or received via API handoff.

Step 2: Profile and classify the data

Cassidy analyzes the dataset structure, profiling columns for distributions, null rates, uniqueness, and referential integrity while classifying fields for PII sensitivity using your defined rules.

Step 3: Validate against data contracts

The agent evaluates the dataset against your schema contracts and expectation suites1checking column types, completeness thresholds, value ranges, freshness SLAs, and cross-field logic.

Step 4: Apply PII protections

For flagged sensitive columns, Cassidy applies the prescribed controls from your Knowledge Base1masking analyst-facing views, hashing identifiers for joins, or redacting prohibited fields entirely.

Step 5: Route exceptions to quarantine

Failed rows or batches are isolated in a sandbox with diagnostic context: which checks failed, sample records, and timestamps1ready for triage without blocking clean data.

Step 6: Generate QA artifacts and notify stakeholders

Cassidy produces validation summaries, coverage reports, and SLA scorecards, then routes results to Slack, Teams, or email with links to detailed findings and remediation runbooks.