7 hours → 2.5 minutes. Our CEO Alexander Soroka just shared a great reminder about the importance of optimization in AI projects.

It’s the same way we approach every challenge in our portfolio - not just training neural networks, but optimizing every critical path in the pipeline.

Example: when working with massive satellite GeoTIFF files, a naive python (sure, it's python for AI research part) patch grid alignment took over 7 hours of preprocessing. By digging into the Python GeoTIFF library, caching metadata reads, and re-implementing geo-transforms, the time was reduced to: • 7 hours → 1 hour (metadata caching) • 1 hour → 2.5 minutes (geo-transform re-implementing)

Production-grade performance isn’t just about the AI model - it’s about maximizing resource utilization at every step to ensure readiness for both vertical and horizontal scaling