Data & AI Platforms

Data warehouses, pipelines, GPU inference and LLM infrastructure, designed for production and run by the same engineers.

Talk to our engineers How we work

Sounds familiar?

Data lives in a dozen systems and every report is a project

Models work in a notebook but not in production

GPU costs keep rising and nobody can say what each model costs

Pipelines break silently and nobody notices until a number is wrong

We build the platforms underneath data and AI work: the warehouse and the pipelines that feed it, and the infrastructure that serves models.

What we build

Data warehouses and ETL pipelines are modeled around the questions your business asks, written as code and orchestrated with Apache Airflow, on Amazon Redshift, PostgreSQL or whichever engine fits. For larger volumes we build ingestion and processing pipelines with monitoring and data-quality checks.

On the AI side we set up GPU inference platforms with autoscaling and cost control, hosting and routing for language models, and MLOps pipelines that take a model from training to production the same way any other software ships.

We run platforms like these for customers today. For a mobility company, for example, we operate the GPU inference platform behind their models.

How we approach it

We start with the questions your business asks and the models you want to serve, and work backwards to the data, the capacity and the architecture. Everything is built as code and documented, and we can run it for you.

Tools

Apache AirflowAmazon RedshiftPostgreSQLKubernetesGPU inferenceLLM servingML pipelinesTerraform

Working with data or models?

Tell us what you want to analyze or serve, and what you have today. We will say what it would take.

Book a 30-minute call