Marketing Personalization · Real-time Analytics
replace batch-oriented, fragmented marketing workflows with governed real-time AI that can personalize campaigns without moving sensitive customer data into external services.
Reported impact
Simon AI customers launch campaigns faster, target audiences more precisely, build contextually relevant audiences 90% faster, and uncover more than 100 new high-value customer cohorts while keeping customer data in a governed environment.
Simon AI built its agentic marketing platform directly on Snowflake's AI Data Cloud so marketing, data, and AI can operate inside one governed environment. First-, second-, and third-party structured and unstructured data stay in Snowflake instead of moving through reverse ETL or external APIs. Snowflake Cortex AI applies row-level LLM logic across billions of events and millions of profiles, while Snowflake data sharing and Snowflake Marketplace enrich customer records with weather, location, and market signals. Engineers build agents in SQL or Python, deploy them with Snowpark Container Services, and use Snowpark plus Dynamic Tables to keep real-time pipelines current. The resulting blueprints assemble reusable agents for intent detection, sentiment analysis, predictive scoring, and campaign activation so Simon customers can launch cross-channel campaigns faster and target audiences with more precision.
First-, second-, and third-party customer data land in Snowflake, where Snowflake data sharing and Snowflake Marketplace add weather, location, and market signals. Dynamic Tables and Snowpark keep live datasets current, Cortex AI applies row-level LLM logic, and agents deployed with Snowpark Container Services generate intent, sentiment, and predictive outputs that feed contextual campaign activation.
Built with Snowflake Cortex AI and powered by Claude from Anthropic
Snowflake Cortex AI allows our agents to apply LLM logic at the row level, in parallel, across massive datasets
building contextually relevant audiences 90% faster
hundreds of newly identified high-value customer cohorts
run directly in Snowflake's AI Data Cloud
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
"numberOfEmployees":{"value":50,"@type":"QuantitativeValue"}
Related
Snowflake Sample Archived · Sep 24, 2026 Foodics Foodics needed near-real-time analytics for tens of thousands of restaurant branches, but older systems struggled with reliability, code-heavy integration work, and the scale of daily reports and jobs. 2M daily reports for over 39,000 customers Snowflake · Snowflake Secure Data Sharing · custom ODBC driver 
Google Cloud Sample Modified · Sep 22, 2026 Imagination AI Imagination AI , part of Imagination Holdings, builds a portfolio of next-generation consumer applications across health, social media, and knowledge management. The company focuses on embedding adaptive, context-aware… Cuts spec run times to 25 mins at ~$0.27 avg cost with Gemini BigQuery · Cloud Run · Cloud Storage Google Cloud Sample Archived · Aug 29, 2026 Demandbase Demandbase needed to automate B2B campaign execution, speed creative production, and give advertisers more direct access to performance insights without depending on slow manual handoffs to customer-success teams. Reduced Demandbase’s internal campaign load to 33% BigQuery · Gemini 2.5 · Gemini platform 