PowderInsights | Snowflake Cortex & AI Consulting

Snowflake has repositioned itself as the AI Data Cloud, and the capabilities that matter now — Cortex AISQL, Cortex Analyst, Cortex Search, and Snowflake Intelligence — all run inside the account, against governed data, with no data leaving Snowflake. That is a genuine shift: the hardest parts of an enterprise AI project (data access, security, cost control) are suddenly the parts you already solved when you adopted Snowflake.

The catch is that "available in the account" is not the same as "delivering value". Semantic models need to be right before Cortex Analyst answers correctly. Search services need the right chunking and attributes before RAG is trustworthy. And a Cortex function inside a SELECT is a credit meter that runs per row. Our senior consultants have built these systems for real teams and know where the sharp edges are.

Cortex enablement

We get your team productive on the Cortex feature set quickly, then stay on for the hard parts:

Cortex AISQL — AI_COMPLETE, AI_CLASSIFY, AI_FILTER, AI_AGG, sentiment, translation, and extraction over your own tables. We design the prompts, the batching, and the guard rails so the results are repeatable and the bill is predictable.
Cortex Analyst semantic models — the YAML semantic layer is what decides whether natural-language questions return correct SQL. We build and test semantic models against your actual warehouse schema, with verified queries for the questions the business asks most.
Cortex Search — hybrid vector + keyword search services over documents, tickets, contracts, and product data, with the metadata filters that keep retrieval governed.

Snowflake Intelligence rollout

Snowflake Intelligence puts a conversational analytics agent in front of business users. Rolling it out well is mostly an enablement and governance exercise: which semantic models and search services the agent can reach, which roles see it, how answers are verified, and how usage is monitored. We run the pilot with a single business team, measure question accuracy, then expand.

RAG on governed data

Retrieval-augmented generation on Snowflake means Cortex Search for retrieval and Cortex AISQL for generation, with the whole pipeline governed by the same roles, masking policies, and row-access policies you already have. We build the ingestion (document parsing and chunking), the search service, the prompt assembly, and the evaluation harness that tells you whether the answers are getting better or worse as you tune.

AI governance and cost controls

Every Cortex function consumes credits, and a careless AI_COMPLETE across a 50-million-row table is an expensive afternoon. We put the controls in before the first production run:

Model selection by task (small, fast models for classification; larger models only where reasoning is needed)
Resource monitors and budgets scoped to AI workloads
CORTEX_FUNCTIONS_USAGE_HISTORY reporting so finance can see cost per use case
Role-based access to Cortex features and to the data behind them
Output logging and evaluation so quality regressions are caught, not discovered by users

Snowpark ML

Not every problem is an LLM problem. For forecasting, propensity, anomaly detection, and classic feature engineering, Snowpark ML (Python) and the Snowflake Model Registry let you train, register, and serve models inside the account. We build the feature pipelines, the training jobs, and the inference paths — and we wire them into the same governance as everything else.

How an engagement starts

Most clients begin with an AI readiness assessment: one to two weeks in which we inventory your data and governance setup, identify three to five concrete use cases, build one of them end-to-end as a working proof, and hand you a prioritised roadmap with cost estimates. From there, the same consultants can stay on to deliver.

All our consultants are senior, US-based, and work directly with your team — onsite or remote.