Data + AI
AI put to work inside Snowflake, on data teams can trust.
Artificial intelligence earns its place when it reasons over governed data inside Snowflake and takes the next action itself, with a person deciding what matters. Proven small first, in production in 8-16 weeks.
How it works
One governed loop, inside Snowflake
An AI agent is software that reasons over governed data and then takes the next action itself, running as a verifiable identity with per-agent permissions and a full audit trail. Cortex Agents run inside Snowflake, next to the data, so nothing is copied out, and every call stays inside the same governed perimeter. A request comes back cited, in policy, and logged.
Inside Snowflake
Reason over governed data, then act
- Cortex Agents + AISQL, next to the data
- Semantic Views: one shared definition of every metric
- Cortex Analyst + Search for cited answers
- AI Agent Identity: per-agent access, full lineage
One request, one governed loop
A rep asks a Cortex Agent in Snowflake Intelligence which accounts are at risk.
The agent calls Semantic Views and Cortex Search, and answers with citations.
The agent drafts the outreach and posts it back to the CRM through a governed endpoint.
The whole exchange is logged against one identity and lineage.
What we ship
Six jobs, running inside Snowflake.
Each one reasons over governed data inside Snowflake and shows up where teams work. Where a real engagement proves it, the numbers are on the card.
Paperwork that reads itself
Claims, invoices, and contracts classified, extracted, and routed in seconds instead of hand-sorted piles. The document backlog becomes a table teams can query.
60→95% accuracy · 4 sec per document
Executives who ask the data directly
Plain-language questions answered with citations against governed definitions, so the Monday meeting starts from the same number, not three versions of it.
Thousands of runaway SKUs, one catalog agent
Churn and failures, flagged early
Models on usage, billing, and sensor data that surface at-risk customers and equipment before the quarter ends, with the reasons attached.
Documents, searched by meaning
Policies, contracts, and wikis answered directly, with sources cited. Retrieval grounded in the organization's own content, so answers stay accurate and current.
Agents that act, inside policy
AI that does the next step, not just describes it: drafting the response, filing the update, kicking off the workflow, each action inside per-agent permissions with a full audit trail.
AI where teams already work
Answers and actions flowing back into the ERP, CRM, and apps the business runs on, so nobody has to visit another dashboard to benefit.
A use case we haven't listed? If the data can carry it, we can ship it. Tell us the job →
Governed by default
One perimeter, one identity
AI governance is not a separate layer here. Inference runs inside the business's Snowflake account, so prompts and results stay within the same perimeter as the data. Every agent acts as a verifiable identity with per-agent permissions, and access, masking, and lineage carry through, with Cortex guardrails screening every call.
- Models run next to governed data: no copies, no export
- Every agent acts as one governed identity
- Access, masking, and lineage inherited from Snowflake Horizon
- Cortex Guard and AI guardrails on every call
- Evals that measure accuracy before a use case scales, not after
Credentials
Certified to run AI on enterprise data
Every model runs under Snowflake's independently audited controls, delivered by SnowPro-certified engineers who do this every day.
Put AI to work where the manual work is.
Tell us the use case. We'll bring the governance and the people to ship it into production with the team, inside Snowflake.