What challenges did Subskribe face before AI implementation?

Diverse financial reporting requests were turning engineering into a bottleneck Manual queries and support tickets were creating recurring overhead The company needed consistent trusted metrics and scalable embedded analytics

What was the impact of the AI solution?

Reduced engineering effort for custom reporting Shifted engineers from support tickets to revenue-driving product innovation Significantly reduced data-access support tickets Enabled faster developer onboarding

Source

GCP

Subskribe — Embedded persona-based financial reporting with Looker + LookML (trusted metrics)

Use case typeConversational analyticsUpdated Jul 11, 2026

Subskribe used Looker’s embedded analytics platform and LookML semantic model to deliver accurate, persona-based financial reporting to customers. The implementation automated data delivery, reduced engineering overhead, improved support efficiency, and created an AI-ready foundation for future conversational analytics.

Organization: Subskribe
Industry: Tech & Comms
Location: United States
Published: July 2026

Reported outcomes

Strategic outcomes

Cost efficiencyReduced engineering overheadInnovation & cultureShifted engineers to product innovationCustomer experience & trustSelf-service persona-based reporting

Primary read

Use case focus

Showing 3 of 3

1Conversational analytics
2Workflow automation
3Data governance

Diverse financial reporting requests were turning engineering into a bottleneck
Manual queries and support tickets were creating recurring overhead
The company needed consistent trusted metrics and scalable embedded analytics

Implemented Looker to power embedded analytics within the product
Built a code-first LookML semantic model with version-controlled business logic
Delivered persona-based self-service views so customers only see relevant metrics
Prepared the data foundation for future conversational analytics and API-based access

Technologies

Looker LookML