Request handling is a challenge for many companies. When operating on the mass market, the company needs to create a department for handling requests. Such departments usually have a lot of work, because requests never end, but they often concern repetitive and schematic problems.
Therefore, we decided to address these challenges and create a tool for streamlining the processing of repetitive requests, which will translate into cost optimization and reduction of handling time.
We are proud to present our product: Mr Wolf. It is a platform which makes it possible to automatically process all types of requests. As it can be easily integrated with external systems, each stage of the process is performed automatically, without human intervention. Out platform makes it possible to handle requests 24/7, regardless of it being a holiday, a weekend or a weekday. Its embedded reporting module offers ongoing insight into the performance of individual processes, as well as the scale of the whole automation. Mr Wolf enables easy addition of processes for automation, management of existing processes and their permissions.
Mr Wolf has been supporting us for half a year in solving users’ requests for one of our biggest clients.
Our platform enabled them to reduce the costs of performing such a service by automating over 30% out of 250,000 requests per quarter.
Main advantages of our solution:
- lowering operational costs
- scalability and resilience against spikes in the number of requests
- 24/7 operation
- easily available history of the processed requests
We address the solution to:
- Contact Center Teams – handling requests from customers
- HelpDesk Teams – handling internal requests
- IT Maintenance Teams – handling incidents and system failures
- HR Teams – automation of employee service processes
How does our system work:
Out system’s operating scheme is:
- First, tasks which are to be processed are classified into categories. Task classification is automatic, and the module responsible for this stage is equipped with Machine Learning mechanisms.
- After the classification stage, the task performance stage starts. Tasks classified for automatic performance are performed automatically.
- The processes themselves are described in Python, which ensures easy description and enables straightforward integration with external services, whether it is a data base, a different application or a website.