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.
Get the Data Foundation AssessmentDiagnostic and architecture work starts at $5,000. Most implementation-heavy foundation projects land between $10,000-$50,000 depending on source sprawl, warehouse maturity, and governance gaps. See details
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
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
Assessment
We audit your current data infrastructure — sources, pipelines, models, and governance — to understand what's working and what's not.
Architecture
We design the target state: data models, pipeline architecture, transformation logic, and testing strategy.
Implementation
We build and deploy using dbt, your cloud warehouse, and open-source tools your team already knows.
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.
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.
Talk Through the Foundation GapsIf the business ask is still fuzzy
Start with Translate the Ask
When the real problem is ambiguity between business needs and data-team execution, the translation sprint is often the smartest way into the broader foundation work.
See the translation sprint
Need a lower-commitment starting point?
Get the framework we use before we touch pipelines or dbt
Most foundation problems are not just technical debt. They are mismatched ownership, weak definitions, and rushed architecture decisions. This framework shows how we sort that out before a warehouse migration or dbt rebuild turns into another expensive cleanup project.
- How we separate tooling problems from trust and governance problems
- The operating questions we ask before recommending warehouse or dbt work
- A simple way to align data, RevOps, and leadership on what has to be fixed first
Instant download. No email required.
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Client Outcomes
Fast-Growing Fintech Startup
Unified 12 data sources into a single trusted warehouse in 6 weeks
A Series A fintech had data in 12 different tools and every team maintained their own spreadsheets. We built a BigQuery warehouse with dbt — tested models, clear documentation — and the CEO went from reconciling spreadsheets to opening one dashboard.
Read case studyVenture-Funded B2B Platform
Migrated from legacy ETL to modern cloud warehouse in 8 weeks
A B2B platform had outgrown their legacy stack. We moved them to BigQuery with dbt in 8 weeks, preserving all business logic and adding proper testing. Their team owns the whole thing now.
Read case studyGo 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