Data and analytics engineering for small and medium sized businesses

We turn messy spreadsheets, CRMs, and product data into dashboards and AI ready datasets your team actually uses.

Scope

• Consolidating data spread across spreadsheets, CRM, accounting, and operational systems.
• Warehouse and pipeline setup sized for a small team rather than an enterprise data org.
• Dashboards built around the decisions the business actually makes weekly.
• Preparing data so AI systems have something reliable to read.

Frequently asked questions

What does data analytics work involve before AI is possible?

Getting records out of spreadsheets, CRMs, and product systems into one place that software can read reliably, then defining the measures the business actually runs on. Most AI use cases fail on data access rather than on models.

Is our data good enough for AI?

More often than owners expect. Data readiness is assessed system by system during the diagnostic, covering where the records live, how clean they are, and whether they can be safely read or written by software. Where the data is not good enough, we say so.

Do we need a data warehouse first?

Not usually. Work starts on the systems you already run, and a warehouse is recommended only when the number of sources or the volume of history makes it cheaper than the alternative.

What do we get at the end of an analytics engagement?

AI ready datasets, the pipelines that keep them current, dashboards for the measures that matter, and documentation. All of it transfers to you.