Session 12-C
How Bill and ZoomInfo Embedded AI-Based Data Observability Into Their Lakehouse
As enterprises modernize their data platforms, open table formats like Apache Iceberg are quickly becoming the backbone of scalable, AI-ready lakehouse architectures. But with this transformation comes a critical challenge: how to maintain real-time data trust across increasingly fragmented, high-velocity pipelines—without introducing latency or operational drag.
This challenge is front and center for organizations like Bill and ZoomInfo. Bill is re-architecting its platform for agentic AI—where autonomous agents trigger dynamic workflows based on chat, APIs, and event streams. This shift requires trustworthy, low-latency data served from systems like Iceberg and Kafka, with observability deeply embedded to support real-time decisions. ZoomInfo, meanwhile, powers its core go-to-market intelligence with a complex, multi-layered pipeline, where even subtle data degradation can break workflows or erode customer trust.
In both cases, traditional, reactive approaches to data quality weren’t enough. Instead, these teams partnered with Telmai to implement a new model: proactive observability at the lakehouse layer. This session will walk through how each organization integrated Telmai into their architecture to continuously monitor schema drift, anomalies, and data contract violations—without slowing down innovation.
Attendees will gain a practical blueprint for embedding intelligent, adaptive data quality workflows into modern data stacks—enabling trusted AI outcomes, resilient pipelines, and faster time to insight.
Speakers
mona rakibe
Co-Founder and CEO, Telm.ai
Aindra Misra
Director of Product Management, Data, AI & Cloud, Bill
hasmik sarkezians
VP of Engineering, ZoomInfo