Context and objectives

Our client, an entity managing public waste collection, faced a recurring problem: handling over 300 citizen complaints each month. Many were irrelevant or duplicate requests, consuming valuable time and creating inefficiencies.

Overwhelmed by the manual handling of these requests, staff members spent nearly one-third of their working hours processing complaints. The emotional toll was quite concerning, with some complaints being aggressive or offensive, adding stress to the team.

Our goal was to test whether an AI-based solution could boost efficiency while integrating seamlessly with the client’s infrastructure. After a successful implementation, automation reduced workload, sped up responses, and freed employees for more critical tasks.

Approach

The project team’s strategy centered around designing an AI assistant to automate most complaint interactions. They first needed to ensure that the assistant could make informed decisions about the validity of each complaint in order to propose a response for the citizen. The system integrated multiple data sources, including:

• Real-time garbage truck location - Tracking each truck’s position to verify if a collection was missed.

• Pre-planned collection routes - Cross-referencing the complaint with the scheduled route to confirm accuracy.

• Collection schedules - Probing whether complaints were logged against the correct dates and times.

Using data already available at our client’s, Agilytic replicated the existing complaint-handling logic into an algorithm that would power the assistant. Built on Azure Virtual Power Agents, the assistant automatically determines their validity and proposes a response that can be validated by the agent and then sent to the citizen. 

A critical part of the process was enabling the assistant to handle unstructured data, especially emails. It needed to extract structured information from the free-text complaints in those emails. The chatbot’s success relied on its ability to process and convert these inputs into actionable data.

Everything was hosted within the client's pre-existing Azure environment to preserve a seamless integration with their existing IT infrastructure and service desk system.

During the testing phase, the team applied weekly patches, which refined the solution's ability to handle edge cases and unexpected complaints. The development team minimized the volume of complaints requiring manual intervention by improving the system iteratively.

The project as a whole has been managed in two phases. Agilytic first worked on a proof-of-concept to validate the feasibility of replicating the complaints handling process with AI. After successfully completing it, we developed a Minimum Viable Product with a more robust and fully integrated solution.  

Results

Over 1.5 months of testing, the AI assistant autonomously handled 83% of complaints. As a domino effect, the automation lightened the administrative workload, freeing staff to focus on more pressing tasks. Secure cloud integration preserved scalability and a reliable, production-ready system for future growth and efficiency.

There is greater potential to build an omni-channel request system. This would unify customer interactions from email, social media, and phone, making it easier for citizens to get quick responses, regardless of how they reach out.

Still, this approach is not limited to waste management. Industries like transport and logistics can also benefit from automating customer service. Other companies could streamline operations using a similar AI assistant system, which can be adapted to various business needs, improving response times and focusing on more complex service challenges.