AI makes good teams faster.

What AI changes

Speed, mostly. Most of our delivery runs AI-augmented: a small senior team ships what used to take a department. Migrations, test coverage, refactoring, documentation, review: work that used to be expensive is now fast. The same speed reaches the product decisions: ideas turn into working prototypes while they are still being discussed, so you decide on something real. This is why Rosenthaler can stay deliberately small and still carry whole product landscapes.

What it does not change

A model can write code. It cannot judge what is safe to ship or carry responsibility when something fails in production. Software that runs a business still needs engineers who understand the business, engineers who put their name on the result. AI without that judgment does not reduce your risk. It accelerates it.

A digital product is more than code. It is deciding what gets built, talking with the people who use it, reading the data, iterating. AI accelerates the building. The product thinking around it, from the first conversation to the next release, stays human work. That is what we mean by product development.

How we work with it

1. 01

   Senior engineers steer. Every line that ships is reviewed, understood, and owned by an engineer, whoever wrote the first draft.

2. 02

   We use it across delivery: code, migrations, tests, documentation, review. You see it as speed and thoroughness, not as a line item.

3. 03

   Boundaries are set per client. Your code and your data are handled within the agreements we sign. They are not fed into tools we have not agreed on.

We also build it

We use AI to build faster. We also build AI itself into products and companies: features, internal tools, and the infrastructure underneath. And we build more than a chat: small, visual cues throughout the product, right where the work happens. That can mean bringing a company’s knowledge into one place and building the tools that work on it. Uncovio shows what that looks like: documents in, structured data out. The vision analysis runs on Innomarks’ backend; we build the product around that intelligence.

We also help teams bring AI into their own development: the tooling, the workflows, and what works in practice. And when the question is still what AI should do in your systems, that is what Product Strategy is for.

What this means for you

The output of a larger team, from a team small enough to know your system completely. Faster delivery without a junior bench learning on your platform. And one thing that has not changed at all: accountability sits with people.

Deciding what AI should mean for your systems? max@rosenthaler.co