We're using AI to write the boring integration code that moves data from System A to System B. The actual data processing is deterministic code that's tested like any critical system.

Correctness: 100% schema mapping accuracy after human validation. We've never had a data type mismatch or field misalignment make it to production. The AI suggests mappings at ~85% accuracy, humans catch and correct the remaining 15%.

Completeness: Zero data loss incidents. We run reconciliation reports comparing source record counts to destination. Any discrepancy fails deployment. Most common issue: the AI initially missing compound key relationships, which we catch in testing.

Tax/Financial Data: Yes, we handle financial data for several clients, including:

QuickBooks to data warehouse pipelines (invoice/payment data)

Payroll system integrations

Revenue reconciliation between CRM and accounting

Our approach for sensitive data:

AI generates the integration logic, never sees actual records

Test with synthetic data matching production schemas

Run parallel processing for 1-2 cycles to verify accuracy

Maintain full audit logs of all transformations

Human sign-off required before production cutover