Rockfish Data announced an integration with Snowflake, the AI Data Cloud company, designed to help telecom operators and network technology providers accelerate the development and validation of Autonomous network operations.
The solution combines Snowflake’s AI Data Cloud with Rockfish’s synthetic data generation platform to produce realistic, privacy-safe network telemetry and observability data directly within Snowflake. Organizations can test analytics systems, automation workflows, and AI models including AI Agents against rare and high-impact network conditions before deploying them into live environments.
Addressing a Critical Validation Gap in Telecom
Telecom networks are among the most complex and high-stakes systems in operation today. While operators collect vast amounts of telemetry and operational data, the scenarios that matter most are-network outages, congestion cascades, signaling storms, and edge-case subscriber behavior-which are often rare, incomplete, or too sensitive to share across teams and vendors. In addition to realizing highly performant and trustworthy autonomous networks, telcos need AI agents and digital twins that validate the impact of their actions before implementing changes into the network.
As a result, AI and automation systems are frequently validated only after encountering failures in production.
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“The telecom industry is rapidly advancing towards AI-driven automation to manage network scale and complexity,” said Sreedhar Rao, Global Telecom CTO, Snowflake. “Yet innovation has been constrained by limited access to realistic validation data. Together with Rockfish, we are enabling carriers and vendors to generate test data within Snowflake’s governed environment-so they can move faster with confidence. Service providers, equipment vendors and ISVs can now get easy, secure access to realistic data to train their telecom domain specific models as well as AI Agents.”
Built for Operators and Network Vendors
Rockfish’s integration with Snowflake is designed to serve both sides of the telecom ecosystem.
For Telecom Operators
- Validate against rare conditions – Test AI/ML models on outage scenarios and edge cases before customers are impacted
- Test automation safely – Evaluate closed-loop automation without touching live networks
- Enable controlled collaboration – Share privacy-safe, realistic datasets across internal teams and third parties
- Accelerate AI deployment – Reduce friction when onboarding and validating network applications and closed loop AI-driven automations
- Build realistic Digital Twins – Create and test Digital Twins with realistic data that emulates the operator specific implementations for training and modeling Autonomous Agents and cross domain closed loop automations
For Network Equipment Providers and Software Vendors
- Prove robustness at scale – Stress test and validate network applications, optimization solutions, and analytics tools
- Reduce time to recreate failure cases for root cause analysis – Eliminate reliance on inconsistent or delayed customer-provided datasets
- Shorten proof-of-concept cycles – Demonstrate system performance faster and accelerate adoption
- Build and test AI Agents at scale – Accelerate AI Agent development lifecycles with robust network datasets for testing and training
“Traditional network testing relies on historical snapshots that fail to capture the dynamic, multi-dimensional nature of modern telecom systems,” said Muckai Girish, CEO, Rockfish. “Rockfish preserves the temporal, causal, and behavioral characteristics that drive real-world network behavior—including rare failure events that may occur once in millions of sessions. By working together with Snowflake, we’re enabling a new standard for AI and Agent validations in telecom.”
SOURCE: Businesswire
