Data, ML and AI engineering services

We are a small engineering studio that designs, builds and runs data, machine learning and AI systems, from the first notebook to a service that is deployed, monitored and maintained.

Projects tend to fail in the gaps between disciplines: a model that nobody can deploy, a pipeline that nobody can test, an LLM feature with no way to tell whether a change made it better or worse. Our work is to close those gaps. Pick the area closest to your problem below, or request a quote and describe what you are trying to ship.

What we do

• Data science

  Analysis, experiments, forecasting and decision support that a business team can act on.

• Machine learning

  Supervised and unsupervised models, trained reproducibly and packaged for production.

• NLP and LLM applications

  Retrieval, extraction, classification and assistants built around language models.

• Computer vision

  Image and video models for inspection, detection, document processing and tagging.

• MLOps

  Pipelines, registries, deployment and monitoring so models stay reliable after launch.

• LLMOps

  Evaluation sets, tracing, prompt and model versioning for LLM features in production.

• DevOps

  Infrastructure as code, CI/CD, containers and observability for data and AI workloads.

• Python and Node development

  APIs, back ends and integrations written in the two languages most AI products run on.

How we work

Every engagement starts with a written scope that says what will be built, how we will know it works, and what you will own at the end. We prefer small, reviewable increments over long silent stretches, and we put the work in your repositories and your cloud accounts so there is nothing to untangle if we part ways.

• Evidence over enthusiasm. We build a baseline and an evaluation first, so improvements can be measured and not just described.
• Boring where it counts. Standard tooling, readable code, tests and runbooks. Cleverness is reserved for the parts of the problem that need it.
• Privacy by default. Data and AI projects touch personal data. We design for minimization and deletion from the start; our background guides on privacy in LLM applications, data mapping and data subject requests explain the thinking.
• You own it. Code, infrastructure definitions, evaluation data and documentation are handed over, with a walkthrough for your team.

Common starting points

• You have a prototype that works on a laptop and need it deployed, tested and monitored.
• You want to add an LLM-powered feature and need a way to evaluate it and keep it from regressing.
• You have data but no clear model, and want an honest assessment of what it can and cannot support.
• Your training or batch jobs are slow, fragile or expensive and need engineering attention.

Not sure where your problem fits? That is a normal place to start. Read how personal data ends up in prompts and traces if you are weighing an LLM feature, or just tell us what you have.