back_office_ops · services · workflow
zeb builds SuperInsight GenAI self-service reporting engine on Databricks, reducing data analyst workload by 80–90%
Enterprise customers of zeb had teams of data analysts manually managing a substantial backlog of data requests, limiting data access for smaller operational teams and creating bottlenecks for analyst capacity.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · User submits data request
End users send a request through email, Slack, or other communication channels.
Tools used
Databricks Data Intelligence PlatformMosaic AIAgent Bricks Custom AgentsMosaic AI TrainingModel ServingUnity CatalogDBRXVector SearchAutoMLSlackTeams
Outcome
SuperInsight reduced data analyst workload by up to 80–90%, delivered 40% cost savings for customers, and drove a 72% uptake in reports requested compared to the previous manual process. zeb also reduced its own solution development time by 40% using the Databricks Platform.
Results
Time saved40%
Volume80–90%
Cost replaced40%
Grounding & classification
Source type: vendor customer story
35 fields verified against source quotes.
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