Business users across finance, marketing, and operations at a Fortune 500 consumer goods company could not get answers from their own data without filing tickets and waiting in analyst queues.
The company needed two things at once: a natural-language analytics tool business users could trust for real financial questions, and a control plane for governing how AI tools get approved, accessed, and audited across a global enterprise. Several competitor attempts at the analytics problem had already fallen short.
Lazer built an internal natural-language BI agent that lets business users query governed Snowflake data in plain English, wrapped in a configuration, versioning, approval, and evaluation layer that controls the context behind every answer. Lazer also delivered an enterprise AI Hub for tool approvals, access-request management, usage analytics, and governance-platform integration.
Business users received correct answers on every query in the financial pilot, the bar that determines whether an analytics agent gets adopted or quietly abandoned.
Questions that previously required engineering tickets or analyst time now resolve in a self-serve natural-language interface for finance, marketing, and operations users.
Tool approvals, access requests, usage dashboards, and governance-platform integration give the enterprise a control plane for every AI deployment that follows, and earned an approved Phase 2.
- How a Custom Manufacturer Reclaimed $500K of Exec TimeStalliant · Manufacturing & Industrial
- How a Mid-Market CFO Halved Board Reporting TimeEventus Advisory Group · Financial Services
- How One Manufacturer Cut Diligence Waits 80% With AIScaylor · Manufacturing & Industrial
- How A Preschool Operator Lifted Labor Productivity 50%SaxeCap · Education & EdTech