How to build Self-Serve GTM Analytics with Gemini CLI on Snowflake using Segment and GrowthCues Core

Empower your Data Engineers with hallucination-proof, text-to-SQL analytics. Learn to connect Gemini CLI to Snowflake data from Segment via GrowthCues Core for accurate GTM insights.

The Context Problem: When AI Hallucinates Your GTM Metrics

In the era of AI, the promise of self-serve analytics—asking natural language questions and getting instant, accurate answers—is compelling. However, connecting Large Language Models (LLMs) like Claude or Gemini directly to your raw Snowflake data often leads to "hallucinations." Without explicit definitions, LLMs guess what "active user" or "churn risk" means, leading to unreliable insights. This "context gap" undermines trust and makes data-driven decisions impossible for your Data Engineers, AI Leads, Analytics Engineers.

GrowthCues Core bridges this gap, providing LLMs with the precise semantic context they need to generate accurate SQL and insights.

Architecture: AI-Ready GTM Analytics with GrowthCues Core

This diagram illustrates how GrowthCues Core standardizes your data from Segment in Snowflake, making it consumable by Gemini CLI for self-serve GTM analytics.

The "GrowthCues Core" Code Block: Defining Context for AI

GrowthCues Core treats "context as code." By embedding metric definitions directly in your dbt schema.yml and persisting them to your Snowflake, you explicitly teach LLMs what your B2B SaaS metrics mean.

Here’s a simplified schema.yml example from GrowthCues Core, showing how a volume_change_ratio_7d (silent churn signal) is defined:

# models/marts/core/schema.yml
version: 2

models:
  - name: fct_account_metrics_daily
    description: "Daily account-level GTM metrics derived from raw product events."
    columns:
      - name: account_id
        description: "Unique identifier for the customer account."
      - name: metric_date
        description: "The date for which the metrics are calculated."
      - name: volume_change_ratio_7d
        description: >
          [Definition] Volumetric Churn Signal: Measures the change in an account's total event volume
          over the last 7 days compared to the prior 7 days.
          [Formula] (SUM(events_last_7d) / SUM(events_prior_7d)).
          [Context] < 0.5 = High Risk (Usage halved). > 1.5 = Expansion Signal.
          Used to detect 'silent churn' where usage drops significantly before cancellation.

This ensures that when Gemini CLI queries your Snowflake, it has an unambiguous understanding of key GTM signals.

Step-by-Step: Implement Self-Serve GTM Analytics with GrowthCues Core

Deploy GrowthCues Core: Clone the GrowthCues Core dbt project and deploy it to your Snowflake, configuring it to consume raw event data from Segment.
Configure AI-Ready Schema: Ensure dbt's persist_docs feature is enabled. This pushes the rich, prompt-engineered metric definitions from GrowthCues Core's schema.yml directly into your Snowflake's metadata.
Connect Your AI Agent: Set up Gemini CLI (e.g., via MCP Toolbox for Claude, or Gemini CLI with system context) to access your Snowflake's data catalog, which now includes GrowthCues Core's semantic layer.
Query GTM Metrics in Natural Language: Your Data Engineers, AI Leads, Analytics Engineers can now ask Gemini CLI questions like "Which accounts are showing signs of volumetric churn risk?" and receive accurate, SQL-backed answers based on GrowthCues Core's definitions.

The GrowthCues Core Advantage: AI-Ready, Open-Source, and Standardized

AI-Ready Data: Eliminates LLM hallucinations by providing structured context directly in your data warehouse.
Standardized Metrics: Ensures a single source of truth for all B2B GTM metrics, solving the "Truth Gap."
Open-Source & Extensible: MIT-licensed, offering full transparency, control, and customization over your core GTM logic.
Reduced Maintenance: Replaces brittle custom SQL with robust, community-driven dbt models.