Snowflake Consulting Services for Enterprise and Life Sciences Data

Standing up a Snowflake account takes an afternoon. Running it well for years means a model the business can query, pipelines that load every source on schedule, roles that match how the organisation shares data, and a credit bill finance can predict.

Our Snowflake consulting services cover the whole lifecycle: a new Snowflake implementation, a migration from an on-premise or cloud warehouse, or a review of an estate that has grown faster than its design. We build in your account, to your standards, and hand over code and documentation your team can run.

Much of our work is with pharma and life sciences teams. Our founder, Harsh Khubchandani, came up through pharma commercial operations consulting at ZS, so we know what syndicated prescription data, specialty pharmacy feeds and HCP masters look like when they land in a warehouse, and what the brand team expects to see at the other end.

Snowflake Cortex is Snowflake's set of managed AI features that run inside your account, so data does not leave Snowflake's governance boundary to reach a model. As of late 2026 the parts we build on most are Cortex AI Functions (SQL functions that classify, extract from or summarise text across a whole column), Cortex Search (retrieval over documents and text fields), and Cortex Agents with Snowflake Intelligence, which became generally available in November 2025, for natural-language questions over semantic views. Snowflake now recommends Cortex Agents over the older Cortex Analyst for new work.

The use cases that hold up are specific: classifying free-text reasons in hub and specialty pharmacy status records, extracting fields from documents, or letting a brand lead ask a governed question of a well-modelled sales table. Each depends on the model underneath being right, so we treat Cortex as the last layer, not the first.

A specialty therapy provider was rebuilding its patient access reporting by hand each cycle, with identifiers that did not match between sources. We built clean identifiers and an MDM structure, automated case pipelines, standardised reporting and dashboards. The result was 60 to 80 percent less manual work and reporting that stayed stable. The same foundations are what a Snowflake estate needs before anything is built on top of it. For more on that layer, see master data management services and specialty pharmacy data analytics.

Snowflake is usually one part of a wider stack. See ETL services and data architecture for the layers around it, Microsoft Fabric and Azure consulting if you are a Microsoft organisation, and pharma commercial analytics for what we build on top for brand teams.