COMPUTER + SNOWFLAKE · BETTER TOGETHER
Snowflake governs your data. Computer completes the agentic control plane.
Snowflake is the governed control plane for the agentic enterprise. But that vision rests on two things: operational data continuously reconciled with Snowflake, and agents that take governed action across the systems where work happens. Both are where the control plane is thinnest — and both are what Computer, by DevRev, supplies.
We don't compete with Snowflake. We complete it — and every system synced drives the consumption Snowflake monetizes.
THE WHY
Where the control plane meets its edges
Snowflake's agentic stack is strong — Cortex, Horizon, CoWork, and the Natoma acquisition all extend it fast. But it's optimized for governed data that lives in Snowflake. The operational context that grounds agents lives outside the warehouse, changes need to flow back to the source, and agents have to complete work — not just connect — across the systems where business actually runs.
01
Data flows in one way
Managed ingestion moves data into Snowflake for analytics, but the systems of record need to stay continuously reconciled — both ways.
Batch, not bidirectional
02
Context lives outside
CRM, ITSM, support, ticketing, code, and chat hold the operational context that explains the business — where the warehouse can't see.
150+ systems of record
03
Connectivity isn't action
Calling a tool isn't the same as a governed runtime that completes work reliably and writes it back to the source at enterprise scale.
Connect vs. complete
COMPARE
Snowflake alone vs.
Snowflake + Computer
An agent detects a ticket spike tied to a defect, correlates it with the churn-risk models already in Snowflake, opens an engineering issue, and updates every affected account's CRM record — governed, and written back to source via AirSync. Snowflake-native and DevRev agents collaborate on the same governed data to resolve the escalation end to end.
Capability | Snowflake alone | Snowflake + Computer |
|---|---|---|
Data reconciliation | Data flows in, one direction. Managed ingestion is built for analytics, not continuous two-way reconciliation with the source. | Continuously reconciled, both ways. AirSync keeps 150+ systems of record — and Snowflake itself — in sync with conflict resolution and write-back. |
Agent reach | Agents in isolation. Without a bridge to operational systems, agents are effective only within the warehouse. | One cross-system memory. Computer Memory unifies every source into a shared graph agents can reason over, cited to source. |
Action | Connectivity, not completion. Reaching a tool is not the same as writing governed action back across 150+ systems. | Governed action out. Skills Engine turns answers into reliable work in the systems where it happens — each side under its own governance. |
WHAT YOU GET
Three layers that complete Snowflake
AirSync — operational data flowing both ways
DevRev's general-purpose bidirectional sync — one conflict-resolving engine across 150+ systems of record plus Snowflake itself. Solves the cold-start problem and writes agent actions back to the source.
Computer Memory — retrieval that holds at scale
An auto-constructed cross-system knowledge graph with hybrid retrieval — Text2SQL, vector, and reverse index. It extends Cortex with cross-system relationships that keep quality as the store grows.
Skills Engine — answers turned into work
CPU and GPU nodes chained in serverless workflows — a governed third-party agent runtime that takes safe action on external systems. The layer that completes work, not just connects to it.
Governed on both sides — your CISO will sign off
Computer connects to Snowflake as a first-class source and target within AirSync. Data moves between the platforms under each side's governance and permissions — never around them.
A consumption multiplier — not another silo
Every AirSync-ingested system drives net-new operational data into Snowflake, and every agent that reasons over it drives query and Cortex compute — consumption-aligned economics that grow with adoption.
ENTERPRISE-BENCH
Accuracy you can trust, cost you can forecast
DevRev built Enterprise-Bench, the first benchmark for enterprise AI agents. Its answer-preserving data scaling holds the correct answer fixed while irrelevant data grows from 40% to 99.84% of the dataset — 256× scaling — so it measures retrieval architecture, not just model reasoning. On identical enterprise tasks, pre-correlated memory beat query-time retrieval on the same data and the same model:
94.3%
Accuracy vs 63.6% baseline
4.4×
Fewer tokens per correct answer
Flat
Cost at scale vs +29%
256×
Data scaling, store still holds
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