AI Automation + Lead Databases QA

Output: A QA gate that catches bad records before they reach your sequences, cutting bounce risk and preventing wasted AI personalization credits.

Time required: 60 to 90 minutes to define criteria and configure the QA logic. Under 5 minutes per list once the gate is running.

1. Define your minimum quality bar before pulling any data

   Required fields: job title, company name, work email, plus optional phone or LinkedIn URL. Set your bounce threshold at 2% or below and define your catch-all policy (pass, fail, or review queue) before pulling any data. Without written criteria, QA decisions will shift on every borderline record.

2. Pull records from your database and export all available fields

   Export with all fields enabled, including primary and secondary email columns where available. Blank fields are a QA signal: a high blank rate means the source does not cover your target persona well. For Clay, use the full-row export to preserve every enriched column for the completeness check in Step 3.

3. Check field completeness against your required field list

   Flag records with empty required fields. A completeness rate below 70% signals poor source fit: re-run enrichment with Clay's waterfall layer (150+ providers) or tighten your ICP filter. Do not pass incomplete records to the verification step.

4. Verify every email address with a dedicated verification tool

   Run SMTP validation, catch-all detection, and disposable domain filtering on every address. Findymail handles all three and targets below 2% bounce rate with 97%+ accuracy. Apply your Step 1 catch-all policy to flagged results immediately.

5. Deduplicate records against your CRM and recent send history

   Match on email first, then on first name, last name, and company domain to catch format variants (first.last@ vs. f.last@). In Clay, build dedup logic as formula columns. Run the check before CRM sync to avoid duplicate records in Salesforce or HubSpot.

6. Score records and route them into passed, failed, and review queues

   Score by rule: required fields complete plus verified email plus no duplicate equals pass. Any single critical failure equals fail; catch-all or partial completeness routes to review. In Clay, run this as a conditional column; in n8n, use a branch node.

Most failures trace back to one cause: QA criteria defined after data is already pulled, or verification run before deduplication removes records that should not consume verification credits.