We build the pipelines that bring your data together, the dashboards and analysis that make sense of it, and machine learning features that do useful work, with a person checking the results.
Pipelines that pull data from your databases, apps and files into one place, tested like any other code so the numbers don't quietly go wrong.
Usually Python and SQL, on AWS, Azure or Google Cloud.
More about data engineering
Dashboards and analysis that answer the questions your team actually asks, plus forecasting and AI-assisted analysis when the data can support it.
Usually Power BI, Python and SQL.
More about data science and AI analytics
Models and AI features built into your products and workflows, like document checks, classification, search, and assistants that work with your own data.
Human review is built in from the start. It's also one of the topics in our co-founder's PhD research.
More about machine learning and AISmall. We pick one question worth answering and one or two data sources, and get a working pipeline and dashboard in front of you early. If it's useful, we build out from there.
What decision would better data help you make? We start there, not with the tools.
We bring in the sources that matter, check the quality, and show you what's really in there.
Once the first piece is useful, we add sources, reports and models, and keep them running.
Pipelines break when a source system changes, and models drift as the world does.
We can look after both on a Managed Services retainer: watching data loads, fixing failures, and updating models with fresh data when they need it. You get the same monthly update as every other client.
Managed Services