Data Foundation

Build a Trusted Data Foundation

Your team spends half its time fixing pipelines instead of delivering insights. Every tool shows a different number. Nobody trusts the dashboards. And when leadership asks about AI, you know messy data will only scale bad decisions. We build data foundations that tell the truth — using dbt, open-source tools, and modern warehouses on GCP and AWS — so analytics, automation, and AI all start from clean, governed data.

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Build a Trusted Data Foundation

How We Help

Data Strategy & Architecture

Design the right approach for your stage, stack, goals, and near-term AI ambitions — including Databricks, BigQuery, and Snowflake on GCP and AWS

Pipeline Development

Build reliable data flows from source to insight

dbt Implementation

Transform your raw data into trusted, tested models

Data Governance

Establish definitions, testing, and operating processes that keep your data trustworthy at scale

How It Works

1

Assessment

We audit your current data infrastructure — sources, pipelines, models, and governance — to understand what's working and what's not.

2

Architecture

We design the target state: data models, pipeline architecture, transformation logic, and testing strategy.

3

Implementation

We build and deploy using dbt, your cloud warehouse, and open-source tools your team already knows.

4

Handoff

We document everything, train your team, and ensure they can maintain and extend the foundation independently. The goal is changing how your organization trusts and uses data — not just delivering infrastructure.

Build a foundation your team can trust and AI can use

  • Reports that match across tools because data flows through one trusted source
  • Pipelines that run reliably so your team can focus on insights, not firefighting
  • Tested, documented data models that the entire organization can trust
  • Clear governance processes that keep data quality high as you scale
  • Clean, well-defined data that makes AI scoring, automation, and copilots safer to roll out

This is for you if...

  • Your team spends more time fixing pipelines than delivering insights
  • You need a partner who understands business context — not just SQL
  • Nobody trusts the numbers because every tool shows something different
  • You’re scaling fast and your data infrastructure can’t keep up
  • Leadership wants AI use cases, but your data is inconsistent, undocumented, or unreliable

This isn't the right service if...

  • You need ongoing database administration or managed services
  • You’re looking for full business system design and integration (ERP, CRM buildouts)
  • You need a large embedded team for 6+ months

Engagements start at $5,000

Scoped and priced upfront based on complexity and business impact. No hourly billing. Most projects range from $10,000-$50,000.

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Client Outcomes

Mid-Market SaaS Data Team

Pipeline reliability from constant firefighting to 99%+ uptime

A 200-person SaaS company’s data team was spending half their time fixing broken pipelines instead of delivering insights. We implemented a dbt-based transformation layer with automated testing, documentation, and clear governance processes. Their data team shifted from reactive maintenance to proactive analysis within six weeks.

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Venture-Funded B2B Platform

Migrated from legacy ETL to modern cloud warehouse in 8 weeks

A venture-funded B2B platform was outgrowing their legacy data infrastructure. We designed and implemented a migration to BigQuery with dbt, preserving business logic while adding data quality tests and documentation. Their team now manages the entire stack independently.

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Fast-Growing Fintech Startup

Unified 12 data sources into a single trusted warehouse in 6 weeks

A Series A fintech startup had customer data scattered across 12 SaaS tools with no central source of truth. Finance, product, and ops teams each maintained their own spreadsheets. We designed and built a BigQuery warehouse with dbt, consolidating all sources into tested, documented models. The CEO now opens one dashboard instead of reconciling four spreadsheets — and the data team spends their time on analysis, not data wrangling.

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Go Deeper

Read our practical guide to building a modern data foundation with dbt — architecture decisions, migration strategies, governance that actually works, and the groundwork for trustworthy AI.

Download the dbt Foundation Guide
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