Simon Data Evolves Marketing with Composable AI Agents Built on Snowflake Cortex AI

Today’s buyers crave personalized experiences that speak directly to their needs and goals — and success for marketers hinges on access to fast, actionable insights. Enter Simon Data, which equips marketing teams with tools to better understand their customers and deliver more personalized, cross-channel campaigns. 

Simon Data’s newest innovation, Simon AI, uses composable AI agents to help teams execute next-generation marketing with greater precision, efficiency and results. Built with Snowflake Cortex AI and powered by Claude from Anthropic, these composable AI agents — which run directly in Snowflake’s AI Data Cloud — give marketers governed, real-time access to first-, second- and third-party structured and unstructured data. These agentic capabilities pave the way for a faster, smarter execution model that’s already helping Simon’s customers better target audiences and slash time to insight.

Marketers are under intensifying pressure to move faster and understand more. Yet many marketing teams still rely on cumbersome legacy systems with batch processing, fragmented data sources and rigid workflows, which hinder access to insights and the ability to respond to changing signals. Often reliant on data and engineering teams to prepare and enrich data, many marketers also grapple with static segments, siloed channels and delayed campaign execution.  

Simon recognized that solving this required more than better pipelines. It called for an entirely new system where marketing, data and AI operate inside the same governed cloud.

With Simon AI, marketers don’t start with a segment. They start with a goal, such as retaining customers, increasing upsells or driving engagement in a specific region. 

Simon AI Blueprints turn these goals into agent-ready workflows: predefined strategies that guide how data is used, which signals to prioritize and what activation logic to apply. These Blueprints assemble the right combination of agents and adapt in real time. They’re fully reusable, allowing teams to plug in new data sources, scale workloads and iterate quickly without rewriting logic. 

Each composable AI agent performs a specific marketing task, such as surfacing intent signals, analyzing sentiment, generating predictive scores or triggering contextual campaigns. These agents work in concert to surface the right data, model precise audiences and activate dynamic campaigns driven by real-time signals. The result: faster time to launch, more precise targeting and dramatically improved campaign performance for Simon customers.

When Simon set out to build composable AI agents, the team needed a foundation that could meet its stringent standards. “After evaluating multiple options, Snowflake’s AI Data Cloud was the only platform that delivered all three of our nonnegotiable requirements: enterprise-grade security and governance, massive scale, and real-time access to data,” says Matt Walker, Co-Founder and CTO of Simon Data. “Snowflake’s capabilities across data engineering, sharing and AI were essential to making Simon AI possible.”

Unlike traditional approaches that rely on external AI services or batch pipelines, Simon’s agents run natively inside Snowflake’s AI Data Cloud, eliminating the need for data movement, reverse ETL or external APIs. Agents inherit the same governance model for customer data, including role-based access controls, lineage tracking and audit trails. This helps ensure security and compliance for data, so clients can run agent-based workflows with peace of mind. Snowflake’s data sharing and the Snowflake Marketplace also allow Simon to enrich first-party customer data with third-party sources — bringing in weather, location and market signals — without leaving the governed environment.

Simon’s platform supports billions of customer events and millions of profiles across behavioral, transactional and contextual data sets. “Snowflake Cortex AI allows our agents to apply LLM logic at the row level, in parallel, across massive datasets,” Walker says. “That means faster execution, lower overhead and the ability to support dynamic, high-throughput use cases, such as real-time segmentation.” 

Personalization loses value when it's delayed. Simon’s agents operate directly on live data streams inside Snowflake, supporting moment-aware use cases like triggering offers from support interactions, reacting to behavioral shifts in-session or triggering a location-based campaign. 

For engineers, Cortex AI has simplified the entire AI development stack. Simon’s team builds agents in SQL or Python, deploys with Snowpark Container Services, and uses standard CI/CD pipelines without managing custom infrastructure. Agents access industry-leading models through a unified interface and scale automatically. With Snowpark and Dynamic Tables, engineers build and manage real-time pipelines directly in Snowflake to ensure agents work with the freshest, most relevant data. Developers can move faster and launch new agents while maintaining control over the deployment lifecycle.

Thanks to Simon AI, Simon’s customers are benefiting from actionable intelligence and agile personalization at scale. Previously, a sentiment analysis workflow could take weeks — extracting transcripts, sending them to an external model and reimporting results. Now, an agent runs in place, and the output is instantly available to update campaigns. One agent, for example, might flag mentions of "school stress" in support calls. Another may generate a “stress signal” attribute. A third may trigger a campaign targeting convenience messaging. 

Another example is a consumer brand that uses agents to monitor weather shifts and launch tailored promotions within hours. Customers in colder climates receive dynamic messaging for winter essentials, while warmer regions see different content. 

These once-theoretical use cases can now be routine with the Simon-Snowflake architecture that powers context-aware activation. Instead of waiting weeks to refresh segments, agents continuously adapt to customer behavior for more relevant and timely customer touchpoints. These insights help brands make decisions in minutes — not days — so they can respond faster to shifts in customer behavior and external events like seasonal trends. 

These advancements are turning into revenue-boosting results for early adopters of Simon AI — from building contextually relevant audiences 90% faster to creating new opportunities through hundreds of newly identified high-value customer cohorts.

As it looks to the future, Simon is expanding its agent ecosystem to support customers using the Snowflake AI Data Cloud. Visual authoring tools will make agent workflows accessible to nontechnical users. Internal marketplaces will allow agent sharing across departments, while benchmarking tools will track agent ROI. 

The goal is to make every brand more adaptive to personalize based on not just customer data, but contextual data that is external to the customer. Simon envisions a world where marketers compose, launch and refine intelligent workflows as easily as they write emails. With Snowflake’s AI Data Cloud as its foundation, Simon can continue to innovate with speed, security and scale every step of the way.