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When scaling at lightning speed, data complexity becomes a major roadblock. Verbit, a leading AI transcription and captioning company and global provider of AI verbal intelligence, knows this firsthand. After acquiring seven companies in just two years, their data team faced a tangled web of infrastructures, repositories, and processes. Even with a unified Snowflake data warehouse, the team struggled with cost efficiency, governance, and visibility across their rapidly evolving ecosystem.
The challenge was clear: optimize costs, streamline operations, and gain control over an increasingly complex data stack. That’s where Seemore came in.
Scaling Pains: The Challenge of a Unified Data Stack
Verbit’s acquisitions meant integrating multiple data architectures, each with its own tools and conventions. This made it difficult to:
- Track dependencies – Understanding how changes in one part of the system impacted downstream processes was nearly impossible.
- Optimize costs – Without deep visibility, costly inefficiencies went unnoticed.
- Govern data effectively – Managing multiple platforms (Snowflake, Rivery, Tableau) at scale was becoming a growing challenge.
The data team needed a solution that would provide real-time insights, automate anomaly detection, and bring full visibility to their sprawling infrastructure.
The Seemore Impact: Cost Savings, Visibility, and Control
70% Savings Through Warehouse Optimization
By integrating Seemore, Verbit quickly identified redundant warehouse configurations, inefficient queries, and excess compute usage. With real-time monitoring and anomaly detection, they could proactively address cost spikes before they became budget killers.
One major win: A single security query was running five times more often than needed—fixing it saved 10% of their yearly Snowflake spend.
Deep Lineage: Full Control Over Data Flows
Beyond cost savings, Seemore’s Deep Lineage provided end-to-end transparency. The data team could instantly see how every change impacted downstream dashboards and processes—something that had previously required time-consuming manual effort.
For example, when an R&D update to a single Snowflake column threatened to break 60+ dashboards, Seemore’s lineage mapping helped the team pinpoint every affected data source, view, and Tableau dashboard in minutes. What could have been a major crisis was resolved swiftly with minimal impact.
What’s Next: Scaling Seemore’s Impact
Encouraged by the dramatic cost reductions and newfound data control, Verbit is now expanding its use of Seemore. The next phase? Optimizing their entire data stack—ingestion, transformation, and beyond—to drive even greater efficiency.
The goal is to extend Seemore’s insights beyond the data team, involving finance, engineering, and other key stakeholders in cost governance and data operations.
