Moviri & Dynatrace
Full IT Observability for a Global Financial Services Brand

Moviri leveraged Dynatrace technology to help one of Europe's largest financial services institutions eliminate critical incidents and increase application reliability and performance.

IT observability built on the Dynatrace platform enables enterprises to manage complex application portfolios.

Our key stakeholder in this project was the customer engineering team responsible for group-wide delivery of full-scale, end-to-end IT solutions.

The Customer manages a broad portfolio of highly-interconnected business applications, built on various technologies spanning the spectrum from cutting-edge cloud-native architectures to legacy technologies.

Download this success story to discover how the Moviri team, led by our performance engineering experts, devised and proposed a plan to quickly identify potential improvements, by tuning application configurations and infrastructure, identify observability blind spots and grant stakeholders easy access to observability data, improving their ability to identify and prevent issues.



Fill out the form to download this case study and you will find:

The customer challenge

An intolerably high number of incidents on the Digital Agency platform.

The Dynatrace solution

Monitor latency, traffic, errors, and saturation across systems.

The customer benefits

Proactively identifying and addressing weak spots before outages occur.

Moviri AI

The Moviri strategy and approach

We put decades of performance engineering work to use for customers.



Monitored services

Monitored services on 50+ business critical applications


Incident risk prediction

Mean Time to Recovery (MTTR) of 3rd party related problems cut by 50%



Reduction in incidents

Kubernetes workloads monitored end-to-end

Our approach
Moviri Analytics

Graph technology for governance, compliance and risk.

We use graph technology to express the relationships between resources and digital data flows taking place within a business process, in real-time. We mapped the ontology in the graph model and developed ​​special investigations and Graph Data Science algorithms to, for example, detect failures, spot wrong system configurations, prevent data leakage, assess risk or detect fraud, and take automatic actions based on the data.


  • Real-time, dynamic monitoring and control of a complex system.
  • Incident impact prediction and reduction.
  • Automatic permission modifications.