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Density’s apprenticeship program started as an internal way to help our own team learn what frontier AI systems can really do. We are now opening that same practice to customers because the lesson has been too useful to keep inside the company.

The program began as a two-week session in our San Francisco office. Distributed Density team members came in person and worked beside a small engineering team that had spent more than a year and a half living with the current generation of AI models. That team had built up practical judgment about where the models are strong, where they fail, and which tools and workflows make them useful in real systems.

The shape of the apprenticeship is deliberately concrete. Participants bring their regular daily work, one ambitious project they always wished an engineering team could take on, and one personal side project that has nothing to do with Density but matters to them. That mix keeps the work grounded in the business while leaving room for curiosity, taste, and momentum.

The bet behind Delights is simple: AI is changing our relationship with data.

The dashboard era treated data as something you went and read at a moment in time. The agent era treats data as something you can talk to, shape, and act on as it moves. We can finally ask the questions we want to ask, in real time, and get the answers in the form we need.

We do not want Delights to be a chatbot pasted on top of a dashboard. We want to make customers’ data legible to the models and ecosystems they already work in. We want to ship products that show what good looks like, and prove that data can be delightful.

The AI landscape is changing quickly. Delights is where we become the trusted partner in continuous exploration.

This post is about the Density CLI and why we are building it.

Density has always measured the physical world. Our sensors count how people use buildings, floors, rooms, desks, and shared spaces. Atlas turns that data into charts and workflows for workplace teams.

But AI changes what customers expect from software. They no longer only want dashboards. They want to ask better questions of their workplace data and get answers they can inspect, trust, and reuse.

• Which rooms are consistently underused?
• Which floors feel busy on Tuesdays but empty on Fridays?
• Where are we paying for space that people do not use?
• What changed after we moved this team?
• Can you make me the chart I would have built myself, if I had three uninterrupted hours?