We ship data and AI use cases to last
We only work on Databricks. That focus shows in what we ship.
What we do
AI to Production
A lot of AI use cases do not mature past the demo stage. That is often because moving them to production afterwards is more work than building them. So when we build a demo, we already set it up the way it would run in production, with tests, monitoring and a proper deployment. On top of that, AI use cases are never done. They also need maintenance after they go live, because the data and the way people use them change over time. We show your team how to keep them running themselves.
Platform Engineering
We design your platform around your company and the way your team works. Databricks also changes fast, with new features every few months. So we set your platform up in a way that it can keep up, and new features can be added without starting over.
Data Engineering
Data pipelines used to be built for people: analysts, reports and dashboards. Now AI agents use the same data when you ask them for insights, and they need different things to get it right. We build pipelines with both in mind.
Advisory & Strategy
Identify the right use cases and define a roadmap that fits your strategic goals. We don't recommend use cases because we can build them, or because the technology is popular right now. We look for the ones your company actually benefits from, and help you decide where to start and what can wait.
Ways to work together
Long term data consultancy
We join your team for the long run. You get a Databricks specialist who knows your platform.
AI use case discovery
We look at where your company could use AI and which ideas are worth building. You get a short list with what each one brings and what it takes.
Ad hoc data consultancy
A short engagement to solve one specific problem or to spar about your platform.
Training
We train your team on Databricks, at the level they are at. We give practical examples so they can apply it straight away.
From the blog and videos
Omnigent part 2: Control and cost
Policies, budgets and Unity Gateway to keep track of what your agents do and spend
Read on SubstackPart 1: A Practical Introduction to Omnigent
What Omnigent is, how it works & how to get started.
Read on SubstackAn introduction to MLflow Open Source AI Platform
How MLflow's core components work together to support the complete lifecycle of generative AI applications.
Read on SubstackDo You Still Need Azure Data Factory in a Databricks Platform?
Exploring whether Azure Data Factory remains necessary when using Databricks for orchestration.
Read on Substack
