About Opsware

Opsware is an engineering studio for data, machine learning and AI systems. We build the models, pipelines, applications and infrastructure, and we publish practical guides on the privacy side of that work.

What we do

  • Data science: analysis, experimentation and forecasting that answer a specific question.
  • Machine learning: models trained, evaluated and shipped as part of a real product.
  • NLP and LLM applications: search, extraction, assistants and automation over text.
  • Computer vision: image and video models for detection, classification and inspection.
  • MLOps: the pipelines, registries and monitoring that keep models working in production.
  • LLMOps: evaluation, tracing, cost and safety controls for LLM applications.
  • DevOps: infrastructure as code, CI/CD, containers and observability.
  • Python and Node development: APIs, services, integrations and tooling.

How we work

  • Start with the problem. We ask what decision or workflow the system should change before choosing a model or a tool.
  • Small, working increments. We aim to put something real in front of you early, then improve it.
  • Measure it. Models and LLM features get evaluation sets and monitoring, so quality is something you can check rather than assume.
  • Leave you able to run it. Code in your repositories, documented, and infrastructure defined as code.
  • Plain scoping. If an approach is a poor fit or something simpler will do, we say so.

What our guides cover

Alongside the services, the site keeps a library of guides on privacy operations for data and AI systems: data subject request automation, data mapping, privacy in LLM applications, security controls, data processing agreements, a regulator directory and a blog.

How we check what we publish

  • Primary sources first. Dates, thresholds and penalties are checked against statute text, regulator websites or official gazettes, and we link to them.
  • Dated pages. Privacy law moves quickly. Each page shows when it was last updated.
  • No invented numbers. Where a tool or example uses assumptions, such as the worked cost example in What a manual data map really costs, the page says so.

The guides are general information. They aren't legal advice and don't create a lawyer-client relationship. For decisions about your own obligations, talk to qualified counsel in the relevant jurisdiction.

Get in touch

To discuss a project, see the contact page.