Modern data platforms
We build the pipelines, warehouse layers and reporting that finance, operations and AI all read from, on Microsoft Fabric, Databricks or Snowflake. Old and new run side by side until the numbers match.
Platforms we build on
Picked to fit the estate you already run.
Where it starts
Sales, finance and operations report three different revenue numbers.
Each team builds its number from its own extract. We define it once, upstream, so every report reads the same one.
Start with: the numbers people argue about, and where each one comes from.
Our best analysts fix the same spreadsheet every month.
The cleanup happens by hand because nothing upstream does it. We move it into the platform, where it runs on every load.
Start with: the manual fixes that repeat every month.
Every new report turns into another integration project.
When each source lands once in the platform, a new report reads what is already there instead of needing its own pipeline.
Start with: the sources behind your most requested reports.
An ERP upgrade breaks every dashboard downstream.
Dashboards wired straight to the ERP break when it changes. With layers in between, the change is absorbed once instead of in every report.
Start with: the dashboards that read your ERP directly.
Our data services
Data engineering work usually starts from something already running: SSIS packages, Azure Data Factory pipelines, a SQL Server or Oracle warehouse and the reports built on them.
Data engineering and architecture
We build in Bronze, Silver and Gold layers: a raw copy of every source, a layer where trust gets built and business-ready data that dashboards and AI read. Each transformation is built once and serves every consumer.
Cloud and platform data migrations
We move warehouses and pipelines to Azure, AWS or Google Cloud and run the old and new side by side until the numbers match, so reporting keeps running through the move.
Serverless data pipelines
For event-driven loads we build pipelines on Lambda, Step Functions, Azure Data Factory and Dataflow, so there are no servers to patch and compute is billed only while data is moving.
Data quality and governance
Quality rules, lineage and access controls go into the pipelines themselves, so an audit question has an answer in the platform and a bad load is caught before it reaches a dashboard.
Analytics and business intelligence
Power BI, Tableau or Looker on governed semantic models, so finance and operations read the same number from the same definition.
If you’re not sure where to start, the first call sorts that out.Talk to us about scope
How we build it
Land the raw data, clean it once and publish the version people and AI models can rely on. The pattern works the same on Microsoft Fabric, Databricks or Snowflake. For most companies already on Microsoft, Fabric is the affordable place to start.
A raw copy of every source. Keep the evidence, so a mistake in your logic doesn’t become a permanent mistake in your history.
Where trust gets built: cleaned, checked and conformed into one definition of each number.
Business-ready data that dashboards, analysts and AI read.
Pick your most valuable sources, stand up the layers and put the effort into Gold. One Gold use case in 90 days, not a full warehouse rebuild.
How this connects to AI
The AI Enablement Guide shows how platform modernization connects to AI readiness.
How we deliver
A defined path from understanding the estate to a platform your organization can use and support.
Review the estate and agree the architecture, first workload and responsibilities.
Deliverable
An architecture and first-workload plan, with owners named on both sides.
Build the pipelines and models, and check the results against the business needs.
Deliverable
Working pipelines and models, validated against the checks the business agreed.
Hand the platform to your team, or keep our engineers on to support it.
Deliverable
Runbooks and a documented handover, or an ongoing support plan.
Industries
Patient, pharmacy and clinical data systems held to HIPAA, including where AI is allowed to touch them.
Clients include
ERP, shop-floor and supplier data on one platform, so plant reporting and finance reporting reconcile.
Clients include
Order, inventory, credit and customer data connected across portals, branches and the platforms behind them.
Clients include
Bank data platforms, reporting that holds up to an audit and managed support for the financial systems the business runs on.
Clients include
Claims, member and policy systems for health plans and insurers.
Clients include
Case studies
Retail
78%
An AI-assisted tool that converts legacy ETL into tested Databricks pipelines. In a three-month first phase across 177 legacy jobs and scripts, it handled 78% of the legacy transformation types, with tests generated alongside every conversion.
Manufacturing
Six operating entities on one SAP ECC 6.0 system, brought into Fabric with no RFC bulk extraction and entity-level security. The client's Basis and SAP teams found the process much easier than they expected.
EV charging
Charger and fleet data from Synop, Geotab and Ampcontrol unified through AWS serverless pipelines into Snowflake, behind a customer dashboard on live data.
Taste of IT · November 18
Chris St. Amand and Chad Puterbaugh on “Wrangling Your Data with the Medallion Architecture.” Bronze keeps every source raw, Silver builds trust and Gold feeds dashboards, analysts and AI, on Fabric, Databricks or Snowflake. Prizes in bronze, silver and gold.
21 days away
Next step
One call about your systems and what needs to change. You get a written proposal if the work fits.
A few practical questions
Questions we hear on first calls.
Ask us about your situationWe start with the business process, then the technology. Map data lineage and dependencies, define data quality rules before moving anything, build incremental pipelines so old and new systems can run in parallel, and validate at every stage with automated testing. We phase the work so each step is validated and can be rolled back.
Data that is clean, governed, accessible and structured for AI to use: consistent formats across systems, documented lineage, quality monitoring with automated alerts and governed access. You can start before the data is perfect, as long as you know which data is trustworthy and build from there.
It comes from three places: lower cost from consolidating redundant systems and automating manual work, recovered revenue ($16M+ in lost Medicaid revenue came back to a health plan after our team noticed a nursing facility file was mishandling records), and a shorter path from data to decision. Name the measurable outcomes before starting.
It depends on your existing stack, your team and how you want to pay for compute. For most companies already on Microsoft, Fabric is the affordable place to start: engineering and Power BI on one capacity, with governance and security included. Databricks is the better fit for heavy engineering, streaming and machine learning, and works alongside Fabric when the work outgrows it. Snowflake works well for SQL teams that want little to administer and need to share governed data. We build on all three.
Related services
Where your data stands, which platform fits and what to build first.
Learn moreQuality monitoring, access controls and lineage tracking built into the platform you already run.
Learn moreAI-assisted modernization, AI on your data and model features built into the applications you already run.
Learn moreMonitoring, fixes and enhancements for data pipelines and the platforms under them, with P1 to P4 response tiers.
Learn moreNext step
Tell us what feeds your reporting today and what has to change. Here is what happens after you reach out.
What runs today, where it breaks and what the business needs from it.
Scope comes from your systems as they are, before anything is priced.
Covering scope, team, approach and price.
Databricks
Claude
SAP Integration
Lakehouse Architecture
React
TypeScript
AWS