The tools are our primary source of data. We do this to reduce the amount of manual entry you would have to do otherwise and save you time. More importantly, in that way the data represents how you truly work and collaborate in real time.

Adadot is an intelligence layer that sits on top of your tactical tools. If its task level information you are looking for the best place for that still remains Jira or Gitlab. If you are however looking for more strategic information for example on how to get faster or better at completing those tasks and how that relates to similar developers Adadot is your go-to.

Adadot is a very low effort way to understand and improve your work patterns. You simply set it up once and it does the work for you. Imagine how much time it would take you to surface the same information manually.

Yes and no. Yes you can write some scripts and get some information. No because Adadot combines metrics from multiple sources and recalculates indexes based on our proprietary data model. We won the most competitive innovation grant in the UK for this; its truly cutting edge and not something you, or a team of engineers and data scientists, can easily replicate.

More importantly, Adadot provides benchmarks on what good looks like by comparing your data to those of similar profiles in our database. That is definitely data you don’t have access to yourself.

No. We do not sell your data. This is why we charge a subscription fee.

No. There are great monitoring tools out there but this is not what Adadot is about. Adadot’s goal is to empower developers and their teams to be the best they can be. We do not show anyone your data and in the team versions the data is aggregated at the team level.

I have a lot of ideas on how to make Adadiot better. How do I get in touch? We love new ideas! Feel free to visit our public roadmap and log them in at

The Indexes are an easy way to get a bird's eye view on your key metrics without getting lost in the detail. Adadot combines and normalises data to a range of 1-10 to make the data more accessible. Weights are also applied to reflect the fact that some data points have more impact than others.

The important thing about benchmarking is to compare like for like. We segment our data based on factors like skills and working patterns to make sure that our benchmarks are a fair comparison.