Customer Support Across Multi-Channel

The main problem occurs when the Chatbot Channel is web-based, as it leads to tedious navigation and hidden accessibility. Moreover, the inconvenience of navigating through web browsers and reluctance to download additional apps hinder a seamless user experience. Another issue is the lack of personalization in interactions with the web chatbot, resulting in a robotic and impersonal feel. Users come from diverse backgrounds with unique communication styles, making it challenging to determine what information is relevant for good customer service. Selecting how to react and personalize interactions further adds to the challenge.

We tackled these issues by implementing middleware that transforms the chatbot's mechanical responses into highly personalized, contextual, and emotionally attuned messages. This transformation was facilitated by cutting-edge large language models, rephrasing chatbot messages based on tone, content, and context from previous interactions. On top of that, we did a similarity analysis of the before and after messages to ensure that the transformation was within our predetermined guardrails.

The simulation setup was not only able to defect >95% of damage types in the simulated environment but also eliminated the need for time-intensive manual labeling, a common bottleneck in real-world data preparation. The labeled images can then serve as input for aMaskRCNN detection model, trained to identify defects with high accuracy. The experience gained in data simulation from'droneInspect' was instrumental in increasing the speed and accuracy of computer vision applications. As a result, this approach has broader implications for future maintenance and inspection-related projects, showcasing its potential to revolutionize the aircraft inspection process and contribute to the advancement of AI-driven solutions.