A Fortune 500 CPG Team Hit 100% Accuracy in Its P&L Pilot

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. The differentiating layer is what surrounds the model: a configuration, versioning, approval, and evaluation system that controls the context the agent uses to answer questions. Versioned, approved context is what turns a plausible-sounding answer machine into a system that returned 100% correct answers in the company's P&L pilot. The client's data leadership reported that users were "getting all correct answers for everything" and called the versioning work decisive against competing attempts at the same problem.

On top of the agent, Lazer built a full enterprise AI Hub: AI governance workflows, tool approvals, access-request management with email notifications, usage analytics dashboards, and integration with a third-party AI governance platform. The build spanned four integrated systems (Retool, Snowflake, a governance platform, and Firestore) and navigated enterprise security review, CISO scope approvals, and release gates on its way to production. The engagement is multi-phase and ongoing, with a second phase approved on the strength of the first.

Lazer started with the hard part of natural-language BI: making answers trustworthy rather than merely plausible. Instead of pointing a model at the warehouse, the team built a control layer around it — a system to configure, version, approve, and evaluate the context the agent draws on, so that only approved definitions shape each answer. The agent was wired to query governed Snowflake data, with Retool orchestrating the application and agent layer and Firestore holding the data architecture. In parallel, Lazer built the enterprise AI Hub that surrounds the agent: workflows for AI tool approvals, access-request management with SMTP email notifications, usage analytics dashboards, and integration with a third-party AI governance platform. Across four integrated systems, the team worked through enterprise security review, CISO scope approvals, and staged production release gates before going live. The engagement was structured in phases, and a second phase was approved on the strength of the first, so the build continues.

Large enterprises where business teams depend on analyst queues for data answers and leadership needs centralized governance over a growing portfolio of AI tools.