Snowflake Intelligence opens a new way to interact with data, letting people ask questions in plain language to get direct, data-driven answers. It’s a major step toward putting data in everyone’s hands, but as with any new capability, its value depends on what’s underneath.
Natural-language analytics only works when the underlying data is consistent, well-structured, governed, and properly documented. If the same metric is defined three different ways, an AI model can return three different answers. To make AI trustworthy, organizations need a layer that defines logic once and carries it through every model.
That’s the role Coalesce plays in the Snowflake Intelligence ecosystem: it ensures the data foundation is reliable before it ever reaches the AI intelligence layer. But Coalesce doesn’t just build clean models. It can project your pipeline metadata into Snowflake semantic views, so Snowflake Intelligence can answer in plain language without drifting from the truth. The same definitions, relationships, and metrics you manage in Coalesce become the guardrails that keep AI answers consistent and auditable.
From raw data to AI intelligence
Snowflake Intelligence relies on the work data teams have already done to ensure raw data has been transformed into clean, governed models that the business can trust. Coalesce streamlines that work by managing how transformations are built, documented, and maintained inside Snowflake.
When business users ask questions through Snowflake Intelligence, the system queries those same Coalesce-built models. The responses are based on vetted, version-controlled definitions that data teams can trace back to source.
This is simply what happens when automation and governance are built into the transformation layer from the start.
Semantic Views: the contract between your data and your questions
Snowflake semantic views give business users a single, trusted place to ask many questions without spawning ad hoc logic. Coalesce can generate these views directly from your pipelines, so the semantics stay tethered to source, versioned in Git, and enforced by design.
How it works
- Select your facts in Coalesce’s gold layer and declare which dimensions they relate to.
- Define metrics and synonyms once centrally.
- Generate the semantic view from pipeline metadata without view-on-view sprawl.
- Ask in plain language with Snowflake Intelligence and see the exact SQL it runs.
- Iterate safely as changes in Coalesce propagate to the semantic view, avoiding drift.
Why this matters
When Snowflake Semantic Views are built on top of Coalesce models, every metric has a single, authoritative definition, so that one definition—and therefore, one question—truly equals one answer. This eliminates the metric drift and conflicting reports that often erode confidence in data.
Further, because Coalesce compiles code to efficient tables/views, there are no additional layers to manage and maintain. And finally, since ownership, lineage, testing, and documentation are already embedded in Coalesce, that governance flows directly into the semantic layer, meaning it is governed by default, not as an afterthought.
The next evolution of data access is here
Most organizations aren’t short on data; they’re short on confidence in their data. Coalesce and Snowflake Intelligence tackle that problem from opposite ends: one ensures the data is built right, the other makes it accessible in a way anyone can use.
Together, we close the gap between how data is prepared and how it’s consumed. For the people responsible for data governance, that connection is what turns AI-driven analytics from a novelty into a dependable part of daily decision-making.
Get ready for Snowflake Intelligence
Organizations don’t need to start over to take advantage of Snowflake Intelligence. The key steps are practical and build on existing data work:
- Start with trusted data. Use the Coalesce-built models that already drive reporting and analytics.
- Verify the logic. Confirm that calculations, owners, and business definitions are documented in Coalesce and reflect current usage.
- Validate semantics. Ensure key relationships and core metrics are defined so the generated semantic view matches how your business speaks.
- Test and iterate. Treat early questions as validation checks. Each refinement in your Coalesce pipelines improves every future AI-generated answer.
This workflow allows data engineers and analysts to stay focused on building the right structures while empowering business users to explore data safely in natural language.
Launch Coalesce on Snowflake Partner Connect
Snowflake customers can experience Coalesce’s full feature set by creating a free trial account directly from the Snowflake platform through the Partner Connect portal. Spin up a project, auto-generate a semantic view from an existing model, and ask your first natural-language questions backed by governed definitions.