QPR has extended the MCP capabilities of QPR ProcessAnalyzer to the Snowflake Native App environment, enabling Snowflake-based AI agents to access Process Intelligence directly where enterprise data already resides.
QPR ProcessAnalyzer, QPR’s Process Intelligence platform, already operates natively on Snowflake. With the latest capabilities, its Process Intelligence can now be made directly accessible to Snowflake Cortex Agents and Snowflake CoWork through the Model Context Protocol (MCP) without unnecessarily moving or duplicating the underlying enterprise data into a separate analytics environment.
The result is a more direct path from enterprise data to Process Intelligence to operational context for AI.
“Enterprise AI needs more than access to data. It needs context that explains how the organization really operates. By bringing Process Intelligence closer to enterprise data and AI within Snowflake, we can help organizations give their AI systems a much stronger understanding of real business operations,” says Matti Erkheikki, CEO of QPR Software.
QPR introduced MCP support for QPR ProcessAnalyzer earlier in 2026, enabling AI agents to access Process Intelligence directly. Extending this capability to the Snowflake Native App environment brings AI agents closer to an operational understanding of how business processes actually run.
Instead of relying only on documents, databases or isolated data points, AI agents can use Process Intelligence derived from actual process execution to answer questions such as:
Where are delays occurring?
Which process variants create the most rework?
What is driving a specific operational exception?
Where is value being lost?
Which improvement opportunities should be prioritized first?
“The real value emerges when AI can move from simply retrieving information to understanding what is happening across business processes and why. Process Intelligence gives AI agents that operational perspective. Bringing these capabilities into the Snowflake Native App environment is an important step toward simpler and more scalable enterprise AI architectures,” says Olli Vihervuori, CTO of QPR Software.
Keeping Process Intelligence close to enterprise data can reduce unnecessary data movement and the additional architecture, governance and security considerations that come with separate analytics environments and integration layers.
For enterprises already using Snowflake, the path becomes increasingly direct:
Enterprise data → Process Intelligence → Operational context → AI agents
This supports QPR’s broader vision of Process Intelligence as the operational context layer for enterprise AI — helping AI understand not only what enterprise data contains, but how business operations actually work.
For further information:
QPR Software Plc
Matti Erkheikki
Chief Executive Officer
Tel. +358 40 717 2570