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The agent
The system

Make Amazon Q act in Snowflake — safely

Amazon Q answers the question — Upware executes the query: scoped, masked, logged, and governed every time it touches your Snowflake warehouse.

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Governed by policy & RBAC Full audit trail Outbound-only on-prem bridge No rip-and-replace
The gap

Amazon Q stops at the Snowflake boundary

Amazon Q connects to many apps, but governed, auditable execution in legacy and compliance-heavy systems needs an execution layer purpose-built for it.

Three ways to close it

Only one is governed

Amazon Q alone
Acts in SnowflakeNo
Governedn/a
Auditedn/a
Custom API glue
Acts in SnowflakeSort of
GovernedYou build it
AuditedYou build it
Amazon Q + Upware
Acts in SnowflakeYes
GovernedPolicy + RBAC
AuditedEvery action
In practice

From a question to a governed warehouse write

A compliance analyst asks Amazon Q to pull Q2 loan-portfolio exposure figures broken down by risk tier. Amazon Q identifies the right dataset and hands the request to Upware. Upware replays the pre-approved, parameterized query against Snowflake under the analyst's RBAC entitlements, ensuring column masking and row-level security policies are honored without re-negotiating them at runtime. Results land in the approved reporting table, and every query — its parameters, the user identity, and the timestamp — is written to the audit log automatically. The analyst gets the answer they needed; the security team gets a record they can stand behind.

The harness

AI to learn. Deterministic to execute.

Upware learns the process once and encodes it as a mostly-deterministic workflow: system interactions replay exactly, and any LLM steps are wrapped in policy, verification, and audit — so execution stays governed and drift-proof.

O
Observe & Learn
Records the real workflow once, across every system it touches.
G
Generate
Builds the API, data flow, and logic automatically — no integration project.
E
Encapsulate
Wraps it as a governed AI skill over secure MCP or API.
R
Run
Executes deterministically — RBAC, audit, and scale built in.
FAQ

Common questions

Does Upware bypass Snowflake's native access controls when Amazon Q executes a query?
No. Upware runs queries through the same Snowflake session and role the action was trained and approved for, so column masking, row-level security, and data-sharing policies all apply normally. It adds a governed execution layer on top of those controls, not a bypass around them. Every query is logged in Upware's own audit trail as well, giving you a second record outside Snowflake's query history.
Our Snowflake environment holds regulated financial and health data. How does Upware keep that data from leaking back to an LLM?
Upware separates the learning phase from the execution phase. At runtime, Snowflake query results travel from the on-prem bridge directly to the requesting application; they are not fed back through a general-purpose LLM. Any LLM involvement is bounded by policy and scoped to the specific action definition, so regulated query results stay inside your controlled environment.
Amazon Q already connects to Snowflake through its built-in integrations. Why do we need Upware on top of that?
Native connectors let Amazon Q read data for answering questions, but they do not govern write-back actions, enforce RBAC at the skill level, or produce an immutable audit trail tied to a specific agent invocation. Upware wraps the full action — read or write — in a deterministic, policy-driven execution that compliance and security teams can audit after the fact. That distinction matters most in regulated industries where 'the agent did it' is not a sufficient audit entry.

Connect Amazon Q to Snowflake

See a governed action go from recorded workflow to live in days.

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