Automation at Databalance: quality as a starting point
At Databalance, we are also committed to automation and efficiency, provided that this goes hand in hand with our standards for quality, security, and reliability. It is precisely because of this conviction that we have set clear preconditions for the use and development of AI applications.
Our principles are:
- Data governance
All data remains within our own data boundaries and under our complete control. - Human in the loop
AI supports and advises, but humans remain in charge and ultimately responsible. - Control & auditability
All output and actions are verifiable and auditable and are continuously tested on a random basis.
These principles ensure that AI within Databalance is always a tool and not a replacement.
From concept to practice: TARS in our operations
Based on these preconditions, we developed and implemented a concrete use case within our IT operations: TARS – Ticket Assistant Review Solution (named after the robot assistant from the movie Interstellar, a favorite among nerds).
TARS reviews tickets submitted by business relations and supports our engineers with additional context, such as:
- possible causes of incidents,
- relevant knowledge sources,
- signs of hidden urgency or priority in the ticket text.
Secure, manageable, and continuously improved
The solution runs entirely within Databalance’s own data boundaries and is built according to our Infrastructure-as-Code principles. In addition, TARS uses modern security components, including Key Vaults and Managed Identities.
From this secure and easily manageable environment, engineers receive targeted suggestions and advice. The output is continuously evaluated and refined where necessary, so that quality is structurally improved and remains in line with everyday practice.
More room for substantive attention
By supporting engineers with relevant context and insights, they can get to the heart of a question more quickly. This results in faster and more consistent support, with more room for substantive attention and personal contact with our clients.
This is how we use AI in a practical and responsible way: not as an end in itself, but as a means to strengthen our services in a sustainable way.