This is one of the most common situations we see. Most organizations have Snowpark licensed but unused because their data engineering foundation isn't ready for ML workloads. Our typical path to production AI starts with data engineering modernization—building the dbt-based feature engineering pipelines and Airflow orchestration that feed ML models with reliable, tested data. From there, we migrate existing Python ML workloads from external notebooks into Snowpark, register models in Snowflake's Model Registry, and deploy scoring pipelines as Snowflake Tasks. For Cortex AI, we activate Document AI for unstructured data processing and Cortex Search for retrieval-augmented generation (RAG). Most customers deploy their first two to three production models within 90 days of starting the engagement. The key is that we build for production from day one—not a proof of concept that dies in a notebook.

Snowflake's professional services are optimized to get customers deployed quickly—they're strong at initial implementation but aren't designed for long-term cost optimization, governance architecture, or production AI enablement. Snowflake-focused boutique partners are often strong at migration and initial deployment but limited in managed services longevity and production AI depth. The large system integrators bring brand recognition but handle Snowflake performance tuning and Snowpark ML through subcontractors. Pythian's differentiation activates when the environment grows complex. We combine 25+ years of managing the world's most complex data environments—Teradata, Netezza, Oracle RAC—with deep Snowflake-specific expertise in clustering key optimization, Snowpark ML pipelines, Cortex AI production deployments, and enterprise governance architecture. We also provide 24/7 managed services, which most partners don't offer at scale. We're the partner you call when generalist firms run out of answers.