Every report and AI agent pulls from the same data.
We build data warehouses, pipelines and models on your existing stack to bring data from every system into one place for reporting, analysis and AI agents.
What we do
PAZ provides data engineering services and data warehouse consulting for companies whose reporting has outgrown spreadsheets and platform dashboards. Each warehouse runs on BigQuery, Snowflake, Databricks or another cloud platform, with automated pipelines that integrate data from ad platforms, Shopify, GA4, CRM, email, finance and operations systems. Our dbt models define revenue, customers, spend and other core metrics once for use in every dashboard and report. Automated reporting built on the warehouse refreshes daily, with no manual spreadsheet exports. For AI agents, we add context engineering: a semantic layer, documented definitions and access rules that keep agents on the same numbers your team reports on.
Warehouse and pipelines
A warehouse on BigQuery, Snowflake, Databricks or another cloud platform, with automated connectors for ad platforms, Shopify, GA4, CRM, email, finance and operations systems.
Data models
dbt models that define spend, revenue and customers the same way in every report.
Dashboards
Dashboards in Data Studio, Hex, Power BI, Tableau, Sigma or another BI tool, refreshed daily from the warehouse and built around the questions your team asks every week.
AI and context engineering
A framework for AI agents built on the warehouse: a semantic layer, documented metrics and access rules that keep every answer consistent with your reports.
How we work
We start with the questions the data needs to answer.
- 01ScopeList the questions, data sources and people who need the answers.
- 02ConnectSet up pipelines from each source, with personal data hashed before it reaches the warehouse.
- 03ModelDefine each metric once in dbt and test it.
- 04Launch and supportLaunch dashboards and documentation, with ongoing monitoring, fixes and new data sources.
What is a data warehouse?
A data warehouse is a central database, such as BigQuery or Snowflake, that stores data from ad platforms, analytics, ecommerce, CRM, finance and other systems in one place. Because metrics are defined once in the warehouse, every dashboard, report and AI agent uses the same numbers.
When does a business need a data warehouse?
A warehouse makes sense when reporting depends on manual exports, when the data is too granular for spreadsheets (keyword-level search data, for example), or when a report has to join several sources such as ad spend, CRM records and revenue. It also helps when platforms disagree on spend or revenue, or when the team needs more history than each platform keeps. Businesses with many locations, brands or markets tend to reach that point first.
Which tools does PAZ work with?
PAZ builds on the stack a brand already uses. A typical PAZ stack is Fivetran for pipelines, BigQuery for the warehouse, dbt for modeling, and Hex and Data Studio for analysis and dashboards. The team also works in Snowflake, Databricks, Power BI, Tableau and Sigma.
What is context engineering for AI agents?
Context engineering gives an AI agent what it needs to answer correctly: clear metric definitions, documented tables, access only to the data it should use, and tests that catch wrong answers. PAZ builds that framework on the client’s warehouse. Agents for reporting and analysis then work from the same definitions as the team’s dashboards. The framework scales as new data sources and agents are added.
Other services
Paid Media
Search, social, programmatic and CTV, managed to ROAS and CAC targets.
Analytics & Reporting
GA4, pixels, event tracking and reporting, set up so every report matches.
Email & Lifecycle
Email, SMS, loyalty and subscription programs that grow customer lifetime value.
SEO + GEO
Technical, content and local SEO, plus visibility in AI answers.
Strategy & Creative
Ad creative, email design and brand strategy, all planned with media.
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