The Scale Challenge

Financial stress testing requires massive datasets—billions of transaction rows. Using an LLM to generate this token-by-token is prohibitively slow and expensive.

The Vectorized Advantage

Misata's Hybrid Engine uses LLMs only for schema understanding. For the actual data generation, we compile rules into vectorized NumPy operations.

| Metric | LLM Generation | Misata Vectorized | | :--- | :--- | :--- | | Speed | 10 rows/sec | 1,000,000 rows/sec | | Cost | $10/million rows | $0.01/million rows | | Consistency | 85% | 100% |

This architecture allows banks to simulate decades of transaction history in seconds.