Casimir Wojcik

Consolidation strategy depends on resolving conflicting data and business rules first.

Data quality decisions during migration become the foundation your AI models will learn from.

Training data gaps, not model architecture, drive AI hallucinations.

Turn eval harnesses into production gates using the right framework and orchestration topology.

The harness, not the model, drives latency and throughput.

Current benchmarks mismeasure multilingual AI ability.

Synthetic test data helps teams catch AI failures before production reaches real users.

Models drift silently in production when training and real-world data diverge.

Unreliable human labels corrupt eval sets and mislead model rankings.