Building a real-time intelligence layer for GTM
Rippling’s mission is to free smart people to work on hard problems. The Growth Engineering team wanted to bring that same philosophy to its go-to-market organization by reducing the manual work required to collect data, write SQL and assemble context before teams could take action.
Sales, marketing and operations teams needed immediate answers to questions that shaped GTM execution, including:
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Which accounts were showing buying signals
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How campaigns were influencing the downstream revenue
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What messaging resonated most with prospects
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Which accounts and contacts represented the best next action
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What context should drive personalized outreach with a compelling reason to help people adopt Rippling
Rippling also needed AI agents to operate on the same trusted data foundation, enabling them to automate analysis and surface insights in real time.
The challenge was that the existing data architecture was built for traditional analytics rather than real-time AI workflows. Data moved through multiple ingestion tools, transformation layers and scheduled pipelines before reaching downstream reporting systems. That introduced latency, fragmented business logic across tools and made it harder to serve governed insights consistently across both employees and AI agents.
As Rippling pushed further into AI-enabled GTM, the gap between reporting and action became harder to ignore. The organization needed a unified foundation where data could be trusted, queried instantly and used consistently by both people and agents.
“We didn’t just want faster dashboards,” said John Kutay, Head of Growth Engineering at Rippling. “We wanted a real-time intelligence layer where both GTM teams and AI agents could operate on the same governed
