Every AI initiative we've seen stall does so for the same reason: the data isn't ready. It's scattered across systems, inconsistently formatted, manually reconciled, or simply not trusted. Before the first model runs, before the first dashboard launches, the data infrastructure has to be solid. That work is unglamorous. We've been doing it for 20 years, and it's still the most important thing we do.
Years in Production
Since 2006. Fortune 15 first client. Every industry on this page, in production.
Your Reporting Can't Be Trusted, and Everyone Knows It.
The meeting starts and someone questions the numbers. Not because they're trying to be difficult; because last month's numbers were wrong, and the month before that, a column was duplicated in the export. Every major report has a disclaimer.
The underlying problem is that your data lives in silos. Your billing system, your CRM, your ERP, your EMR: none of them were't designed to share data, and integrating them was never quite important enough to prioritize. So instead, your team exports from one, manipulates in Excel, imports to another, and prays the formats match.
The AI problem is downstream from this. You can't build reliable AI-driven analytics on top of unreliable data. The organizations that are actually getting value from AI are the ones that fixed their data infrastructure first.
“The meeting starts and someone questions the numbers. Not because they're trying to be difficult; because last month's numbers were wrong, and the month before that, a column was duplicated in the export. Every major report has a disclaimer.”
A common situation we hear in discovery
Infrastructure First. Analytics Second. AI When It's Ready.
A structured process built from 20 years of doing this work.
Data Audit
We assess your current data environment: what systems you're running, how data flows between them, where it gets lost or corrupted, and what your current reporting actually depends on.
Architecture Design
We design the target state: a data warehouse or lakehouse architecture appropriate for your scale, with ETL pipelines that pull data from every relevant source, transform it consistently, and load it into a governed, queryable store.
Pipeline Build
We build ETL pipelines using Apache Hop, custom integrations, and SQL Server or cloud database targets as appropriate. Pipelines are scheduled, monitored, and built with documented transformation logic, not black boxes.
Analytics Layer
We build the reporting layer on top of clean data: dashboards, scheduled reports, ad-hoc query access, and the data models that support your business intelligence needs.
AI Readiness
For clients planning AI or ML workloads, we structure the data infrastructure to support model training, inference pipelines, and AI-driven analytics. Built into the architecture from the start.
What You Get
Concrete outcomes from every engagement.
Single Source of Truth
One place where your business data lives, reconciled, consistent, and queryable without manual intervention.
Reliable ETL Pipelines
Scheduled, monitored, and built with clear transformation logic. When something breaks, you know immediately and why.
Reporting You Can Trust
No more disclaimers on the data. Reports run on governed data with documented lineage.
AI-Ready Infrastructure
Data structured and governed to support machine learning workloads, LLM-based analytics, and automated decision workflows.
Full Documentation
Every pipeline, every transformation, every data model is documented. The next engineer who touches this will understand what they're looking at.
Query Performance
Data warehouses designed for performance. Reports that used to take 20 minutes to run, running in seconds.
Technologies We Use
Tools selected for fit and reliability, not to pad a capabilities list.
ETL & Pipelines
Databases & Warehouses
AI & Analytics
Infrastructure
A Representative Scenario
How this type of work plays out in practice.
The Situation
A multi-location urgent healthcare practice was pulling reports from three separate systems (their EMR, their billing platform, and a scheduling tool) via manual exports, combining them in Excel, and producing a weekly operations report that took approximately 6 hours to compile and was frequently questioned in leadership meetings due to data inconsistencies.
What We Did
Built a unified data warehouse with ETL pipelines pulling from all three source systems, with transformation logic that reconciled patient, billing, and scheduling data into a single governed store. Built a reporting layer on top that produced the weekly operations report automatically, along with daily dashboards that previously didn't exist.
The Result
Weekly report compilation dropped from 6 hours to automated. Data inconsistencies that had been debated for months were identified, root-caused, and resolved. Leadership gained same-day visibility into operational metrics. The infrastructure also served as the foundation for subsequent AI-driven analytics work.
Common Questions
Things clients typically want to understand before starting a conversation.
Let's Start with Your Data.
Whether you need a full data warehouse or just reliable ETL pipelines from your current systems, it starts with understanding what you have. Book a free consultation and we'll give you an honest assessment of your current data environment.