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

Teams that once waited in analyst queues now ask their governed data in plain English and trust the answers, while leadership governs every AI tool from one control plane.

100%

Answered P&L pilot queries right

Implementation Time

Aanikh Kler

Lazer Technologies | Co-Founder Surf (acquired) | Canada's Young Entrepreneur of the Year 2023

Lazer Technologies

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The Challenge

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.

What They Built

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.

AI Role

An LLM-powered agent translates natural-language business questions into governed queries against the enterprise data warehouse, operating only on versioned, approved context. The surrounding hub governs the AI itself: which tools are approved, who can access them, and how usage is audited, making the AI estate manageable rather than merely impressive.

AI Model

To be confirmed

Infrastructure

Retool (application and agent layer) • Snowflake (governed data warehouse) • Firestore (data architecture) • Google Workspace; SMTP notification flows

Integration Points

4 systems integrated: Retool, Snowflake, Credo AI governance platform, Firestore • SMTP access-request notification flows • Versioned/approved context layer controlling agent answers

Impact

100% Query Accuracy in the P&L Pilot

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.

Tickets Eliminated From the BI Path

Questions that previously required engineering tickets or analyst time now resolve in a self-serve natural-language interface for finance, marketing, and operations users.

An AI Governance Layer, Not Just a Tool

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.

Technology Utilized

Implementation Complexity

Time Savings

Risk & Compliance

Best Fit For

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

Aanikh Kler

Lazer Technologies | Co-Founder Surf (acquired) | Canada's Young Entrepreneur of the Year 2023

Lazer Technologies

Award-winning entrepreneur and tech leader helping startups and global brands like Netflix and Amazon drive growth by ethically harnessing consumer data and AI-driven innovation.

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Industry:

Consumer Goods & CPG

Business Function:

Finance & Accounting

Operations

Executive & Strategy

Company Size:

5,001+

Project Cost:

Cost not disclosed

Ownership:

Public Company

Organization Type:

Public Company

AI Pattern:

Conversational AI (Chatbot / Agent)

Data Synthesis & Reporting

Decision Support & Scoring

Value Type:

Time Savings

Risk & Compliance

AI Model:

Frequently Asked Questions

How did a Fortune 500 CPG team get accurate answers from an AI analytics agent?

Lazer wrapped the natural-language agent in a system that versions, approves, and evaluates the context it uses, so it answers only from approved definitions of the data. In the company's P&L pilot, business users received correct answers on 100% of queries.

What AI tools and approach did the natural-language analytics agent use?

The agent was an LLM-based system orchestrated in Retool that queried governed data in Snowflake, with Firestore in the data architecture and a third-party AI governance platform for oversight. The source does not name the specific underlying LLM.

What results did the AI analytics agent deliver for the CPG company?

In the P&L pilot, users received correct answers on 100% of queries. Questions that once needed engineering tickets or analyst time became self-serve for finance, marketing, and operations, and the surrounding AI governance layer earned an approved second phase.

How long did the AI analytics agent take to implement?

The engagement is multi-phase and ongoing. The first phase shipped through enterprise security review, CISO scope approval, and production release gates, and a second phase has been approved. The source does not give a specific timeline.

Who is a natural-language analytics agent with AI governance best for?

It fits large enterprises where business teams rely on analyst queues for data answers and leadership needs centralized governance over a growing portfolio of AI tools.