Industries
Technology for companies built on data.
Technology and media companies live and die by engagement, retention, and speed of delivery. We help you predict churn, personalize experiences, and ship faster, with the data and delivery foundations to back it.
Book a consultationWhat we do
From retention to delivery.
In this sector data is not a back-office asset. It is the product and the growth engine, which changes what good looks like.
Customer churn prediction
Models that flag at-risk customers early, so you can intervene before they leave rather than react after.
Recommendation engines
Personalization that surfaces the right content, product, or feature at the right moment, lifting engagement and revenue when it is done well.
Portfolio management
Prioritizing and managing complex product portfolios, aligning investment to value and keeping delivery focused on what moves the needle.
Agile enablement
Building fast, product-focused delivery teams with the practices and cadence that turn speed from a goal into a habit.
DevOps enablement
The pipelines and platforms behind continuous delivery, so your teams ship safely and frequently without the manual overhead.
Data foundations
The clean, governed pipelines and platforms that churn models and recommendation engines actually depend on.
Proof, not promises
A Forbes-recognized game studio, MVP in eight sprints.
The studio needed to prove a new product without spending a year finding out whether it was worth building. Working lean, with the scope held honestly and the team coached as it went, they shipped their MVP in eight sprints.
Read the case study →Common questions
Questions we hear often.
How is AI used in technology and media?
Most commonly to drive growth and retention: predicting churn, personalizing content and product through recommendation engines, and analyzing engagement, alongside the agile and DevOps practices that let these companies ship quickly.
How does churn prediction actually work?
It scores customers on behavioural signals that historically preceded people leaving. The model is the easy part. The value comes from having an intervention ready and a team that acts on the score.
What makes a recommendation engine worth building?
Enough interaction data to learn from, and a surface where a better recommendation changes behaviour. Without both, it is an expensive way to reorder a list.
Can you help us ship faster without breaking things?
That is usually a delivery and pipeline question rather than a tooling one. We work on the cadence, the team topology, and the DevOps foundations together, because fixing one without the others does not hold.
Next step
Turn your data into growth.
Book a consultation and we will find where the work moves your engagement and retention.
Book a consultation