THE IMPLEMENTATION PROCESS

We Show Our Work.
Every Step, Explained.

Most data consultants hand you a dashboard and disappear. Sauzi shows you exactly what we're building and why. No black boxes.

The Two Ingredients of Every Sauzi Implementation

The right ingredients, prepared in the right order. Skip either one and the whole thing falls flat.

Ingredient One

Warehouse Configuration

We configure dimension and fact tables so AI agents can reason about your business data. This structure tells the AI what data exists, how it relates, and what questions it can answer. Without it, the AI is guessing.

Dimension Tables Fact Tables Snowflake dbt Models
Ingredient Two

AI Tool Connection

We connect your structured warehouse to Claude Code, Cowork, and other AI tools. Once live, your team asks questions in plain English and gets answers backed by your actual data — not hallucinations.

Claude Code Cowork Natural Language Live Queries

The Path to Get There

The path looks different depending on where you're starting. But every implementation follows the same three-phase structure.

1
Phase One

Foundation

For companies without a warehouse or with unreliable data. We connect your sources, build the ETL/ELT pipelines, and configure your warehouse. You can't deploy AI on a leaky pipe — so we fix the pipe first.

What this phase covers

Source Connections

Connect Shopify, Recharge, Mailchimp, Magento, or whichever platforms your data lives in today.

Pipeline Build / Repair

Build new ETL/ELT pipelines from scratch, or diagnose and repair existing ones that are dropping data or producing errors.

Warehouse Setup

Configure Snowflake (or your warehouse of choice) with the proper schema, access controls, and staging layers.

2
Phase Two

Structuring

This is the step that separates working AI implementations from ones that don't. We build the dimension and fact tables — the semantic layer — that tells AI agents how your data is organized. Without it, AI can't distinguish a customer from an order.

What this phase covers

Semantic Layer

Define business metrics — LTV, CAC, retention, Weeks of Cover — in a way AI agents can understand and query consistently.

Dim / Fact Modeling

Build the dimension and fact table structure that gives AI agents the context they need to reason about your business.

Medallion Architecture

Organize data into bronze (raw), silver (cleaned), and gold (business-ready) layers so every query draws from trusted, validated data.

3
Phase Three

AI Connection

We connect your structured warehouse to the AI tools that query it. Your team asks questions in plain English — "What's our repeat purchase rate this month vs. last month?" — and gets answers in seconds.

What this phase covers

AI Tool Integration

Connect Claude Code, Cowork, or the AI tooling that makes sense for your team directly to your warehouse.

Natural Language Interface

Your team asks questions the way they'd ask a colleague. No SQL required. No tickets. Just answers.

Anomaly Detection

AI agents surface unusual patterns automatically — before someone manually checks a dashboard and notices something is off.

What Makes This Different

The process matters. Here's how Sauzi actually works — not how consultants claim to.

We document what we build

Every implementation includes full documentation of the data model, pipelines, and connections. Your team will always know what's running and why.

We work with your existing tools

We configure your warehouse of choice and connect the AI tooling that fits your team. No rip-and-replace. No platform lock-in.

We meet you where your data is

Some clients need a full build from scratch. Others just need better structure on what already exists. We scope accordingly.

Want to see this applied to your stack?

Tell us where your data stands. We'll map the exact phases, timeline, and what your team gets.

Get My Data Readiness Map