How Fireblocks Uses Snowflake AI Agents to Handle 40-50% of Data Queries

Fireblocks sits beneath a significant portion of the world’s digital asset economy, providing the infrastructure that secures cryptocurrency transactions and stablecoin flows for hundreds of millions of end users. At the scale the company operates—billions of rows of on-chain and transactional data spread across 15 distinct data domains—keeping analytics responsive and accessible was becoming an existential challenge rather than a nice-to-have.

Before adopting Snowflake, Fireblocks was hitting hard limits. Basic queries against tables with billions of rows were taking up to 20 minutes to return. The data team was fielding a volume of requests from product, sales, and engineering teams that manual analysis could no longer satisfy. Siloed data domains meant a sales representative seeking revenue insights and a product developer needing staking account data had to go through entirely different pipelines—with no shared context between them.

The company rebuilt its data platform on Snowflake, running on AWS, and began routing all data—from Salesforce CRM analytics to Zendesk support insights—through the AI Data Cloud. With Snowflake’s unified semantic layer across all 15 domains, teams across the business gained a single, queryable source of truth. The most significant step forward came in a 45-day internal hackathon: engineers built Fire Genie, an AI agent powered by Snowflake Cortex Agents with semantic views, which lets customers query their own wallet and transaction data in natural language through a secure API. A parallel internal agent, built on the same architecture, extended the same capability to Fireblocks’ own staff. Streamlit dashboards on AWS allowed teams to share live, auto-updating reports in place of static screenshots.

The numbers from the internal agent alone tell a compelling story. It now handles over 2,000 analysis queries per month, accounting for 40–50% of all analytical queries company-wide—saving an estimated two full-time analysts’ worth of capacity every month. The data team shifted from being a bottleneck for basic requests to focusing on advanced insights that directly influence product development and customer retention.

Fireblocks is now expanding Fire Genie to let customers build personalized dashboards, while pushing toward a single internal self-service analytics environment where any employee can take a query from initial idea to complete final report without waiting on the data team. For a company whose competitive advantage depends on helping clients trust and understand their digital assets, the ability to put accurate, real-time data in anyone’s hands is not just operational improvement—it is product differentiation.