What Snowflake, Anthropic and Salesforce got right, and wrong, about semantics
Semantic layers, especially auto-generated ones, are becoming the foundation for AI in the enterprise, and now everyone sees it.
Over the last couple of weeks, three things happened that are worth putting together.
Snowflake announced Cortex Sense, a foundational context layer for CoWork that “automatically learns how a business defines its data and operations.” Cortex Sense uses signals from query history, metadata, dashboards in Power BI and Tableau, and enterprise data outside Snowflake to understand things like revenue definitions, fiscal calendars and snapshot tables. Snowflake also shared a pretty incredible internal test: CoCo and CoWork reached 83% accuracy with Cortex Sense, compared to 47% without it and 23% for frontier coding agents with Snowflake MCP. (snowflake.com)
Anthropic published a post on how they use Claude for self-service data analytics. The point was not “Claude can write SQL now.” The more interesting point was that analytics accuracy is mostly a context and verification problem. Their team wrote that the central challenge is mapping a user’s question to the right entities in the data model and knowing how to work with them. Once that context exists, writing the SQL becomes the easier part. (claude.com)
And Marc Benioff recently made the point that AI needs a semantic layer to work well. Salesforce has also been pushing this publicly through its Open Semantic Interchange work, arguing that agentic AI requires a realignment around the semantic data model, and that agents need trusted semantic definitions to translate intent into accurate outputs. (atscale.com)
Enterprise AI needs semantics. Which is exactly what we’ve been saying for over a year.
Recap: why semantics are needed (feel free to skip)
The problem with AI and enterprise data is not just Text2SQL. It is not enough for a model to generate a syntactically valid query. The real challenge is understanding what the user actually meant, which data should be trusted, and how the business defines the thing being asked about.
When someone asks for ARR, do they mean booked ARR, billed ARR, active ARR or ending ARR? When someone asks for churn, are they asking about logo churn, gross revenue churn, net revenue churn, voluntary churn or something else? When someone asks about “active users,” what actually counts as active?
The answer is rarely obvious from the schema. A table name can help. A column name can help. But the real meaning is usually spread across dashboards, SQL queries, dbt models, BI tools, tickets, documentation, Slack threads and people’s heads.
That is why AI needs semantics.
We wrote about this in Autogeneration of a semantic layer - the key for AI/BI, where we argued that AI needs a business-aware layer between users and data.
Manual creation of semantics will get you nowhere
We wrote about it in Semantic layer for AI: let’s not make the same mistakes we did with data catalogs: the obvious failure mode is already familiar: companies will try to manually document everything, and the documentation will immediately start going stale.
That is the important part. The semantic layer AI needs cannot be another manual project.
Enterprises have too much data, too many systems, too many dashboards, too many metric definitions and too many edge cases. A small team can manually define the top 20 metrics. Maybe the top 100. But enterprise AI needs much more than that. It needs to understand the full structured data estate, including cloud and on-prem systems, old and new platforms, BI and warehouse layers, operational systems and the business workflows around them.
This is where auto-generated semantics become necessary.
Not because an LLM should blindly invent your business definitions. That would be a terrible idea. Anthropic is right to point out that auto-generated metric definitions can look plausible while preserving the exact ambiguity you were trying to remove.
The answer is not “let the model guess.”
The answer is to generate semantics from the enterprise’s existing evidence: actual queries, dashboards, lineage, metadata, transformations, documentation, usage patterns, tickets and business context. Then score that evidence, reconcile conflicts, surface uncertainty and let humans review, approve and own the definitions.
That is very different from asking AI to hallucinate a metric layer from raw tables.
But building an automated ain’t easy (we should know…)
A heavily used executive dashboard connected to a curated model is a strong signal. A random query from three years ago is a weak signal. A dbt model with tests and lineage is a strong signal. A stale dashboard nobody opens is probably not. A ticket explaining why a metric changed last quarter may be more useful than a column description written five years ago.
The semantic layer should learn from all of this.
And it should keep learning, because the business keeps changing.
This is where Solid is leading.
Solid was built around the belief that enterprise AI needs an enterprise-wide semantic foundation. Not a narrow layer inside one warehouse. Not a semantic model locked to one BI tool. Not a manual spreadsheet of metric definitions. A semantic layer that spans the full structured data set of the enterprise, across cloud and on-prem systems, and connects technical metadata with business meaning.
That is the future Snowflake, Anthropic and Salesforce are all pointing toward.
Snowflake is saying agents need context. Anthropic is saying analytics needs trusted mappings between questions and data models. Salesforce is saying agentic AI needs a semantic layer.
We agree.
We would just add one more thing: in the enterprise, that semantic layer has to be generated, continuously maintained and connected to the full data estate. Otherwise it becomes another partial artifact that starts clean and slowly drifts away from reality.
AI will not succeed in the enterprise by guessing what the business means. It will succeed when it has a trusted semantic foundation.
That is what we’ve been saying for over a year. And that is what Solid is building.