Navigating to smart solutions across domains

  • Passenger preference analytics

    Airline passengers are catered food in compact trays that are collected back and placed in a trolley once the passengers have consumed the food. For a global player in the airline catering domain, we developed a machine vision solution that leveraged data from these multiple trays, airlines, routes, etc. to build analytical information to derive actionable insights for decision-makers. Analyzing the likes and dislikes of every passenger pertaining to the food items would help airlines take proactive and appropriate decisions in terms of providing food tray items in accordance with each preference, moreover, focusing on customer delight, a key determinant for a healthy CSAT score.

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    Passenger preference analytics | Nagarro
  • Passenger preference analytics | Nagarro

    Airline passengers are catered food in compact trays that are collected back and placed in a trolley once the passengers have consumed the food. For a global player in the airline catering domain, we developed a machine vision solution that leveraged data from these multiple trays, airlines, routes, etc. to build analytical information to derive actionable insights for decision-makers. Analyzing the likes and dislikes of every passenger pertaining to the food items would help airlines take proactive and appropriate decisions in terms of providing food tray items in accordance with each preference, moreover, focusing on customer delight, a key determinant for a healthy CSAT score.

    Explore other smart solutions

  • Viewer engagement and analytics

    A leading network provider in US has various digital signage platforms installed at stadiums for running advertisements. They were looking for ways to engage the viewers by providing relevant content based on real-time audience profiling. We helped them with an android-app based solution by leveraging the machine vision approach and enabled real-time responsiveness in the platform. ​As part of the solution:

    • HD cameras were installed on top of the platform to capture live video stream​s.
    • Machine vision-based solution was built to capture viewer/crowd category, based on various attributes like age, gender, race using live video streams​.
    • Built the capability of gesture control using machine vision​

    Our solution detects and classifies viewers automatically with an accuracy of more than 90 percent. This analysis is being used to trigger appropriate advertisement content. ​

    Viewer engagement analytics | Nagarro
  • Viewer engagement analytics | Nagarro

    A leading network provider in US has various digital signage platforms installed at stadiums for running advertisements. They were looking for ways to engage the viewers by providing relevant content based on real-time audience profiling. We helped them with an android-app based solution by leveraging the machine vision approach and enabled real-time responsiveness in the platform. ​As part of the solution:

    • HD cameras were installed on top of the platform to capture live video stream​s.
    • Machine vision-based solution was built to capture viewer/crowd category, based on various attributes like age, gender, race using live video streams​.
    • Built the capability of gesture control using machine vision​

    Our solution detects and classifies viewers automatically with an accuracy of more than 90 percent. This analysis is being used to trigger appropriate advertisement content. ​

  • Evaluating customer experience

    For a leading Asian airline company, we helped manage and analyze volumes of customer reviews received (in multiple languages e.g. Chinese, Korean, Japanese, and English) from different flights with the help of Machine Learning. The data was being analyzed with manual processes which resulted in significant delays in identity intervention. With modern big data architecture, we provided an automated advantage that ensured real-time ingestion and processing of the data received. Using state-of-art machine learning modes, we identified important business categories, quality of service entities, and sentiments towards these entities. More so, the insights generated were aggregated and organized in a digitized visualization dashboard and analytics that aids better decision-making.

    Evaluating customer experience | Nagarro
  • Evaluating customer experience | Nagarro

    For a leading Asian airline company, we helped manage and analyze volumes of customer reviews received (in multiple languages e.g. Chinese, Korean, Japanese, and English) from different flights with the help of Machine Learning. The data was being analyzed with manual processes which resulted in significant delays in identity intervention. With modern big data architecture, we provided an automated advantage that ensured real-time ingestion and processing of the data received. Using state-of-art machine learning modes, we identified important business categories, quality of service entities, and sentiments towards these entities. More so, the insights generated were aggregated and organized in a digitized visualization dashboard and analytics that aids better decision-making.

  • Conversational agents

    A leading provider for door security solutions wanted an intelligent alternative that could help reduce their call volume for technical issues frequently faced by their customers. Nagarro designed a chatbot that responds to customer queries as well as troubleshoots issues. Also, we developed an optimized conversation flow management portal, which ensures seamless call transfers to human agents whenever required. Within 6 months of implementation, the resolution rate increased by 20% and saw a hundred percent increase in the message coverage rate.

    Conversational agents | Nagarro
  • Conversational agents | Nagarro

    A leading provider for door security solutions wanted an intelligent alternative that could help reduce their call volume for technical issues frequently faced by their customers. Nagarro designed a chatbot that responds to customer queries as well as troubleshoots issues. Also, we developed an optimized conversation flow management portal, which ensures seamless call transfers to human agents whenever required. Within 6 months of implementation, the resolution rate increased by 20% and saw a hundred percent increase in the message coverage rate.

  • Fault detection at scale

    For our customer, a leading telecom player provides services that consist of various network nodes and elements (NEs), fault detection, and localization in network equipment using traditional methods was a challenging and time-consuming task. ​We partnered with them to build a holistic, scalable AI/ML-based product which included the following features:​

    • Data collection and ingestion at a large scale from a variety of heterogeneous network elements (NEs)​.
    • ML Models to determine the health of the NEs and to provide correlation with configuration changes​.
    • A mechanism to intuitively visualize and identify the nodes at fault and help them take proactive action​.
    Fault-detection-at-scale | Nagarro
  • Fault-detection-at-scale | Nagarro

    For our customer, a leading telecom player provides services that consist of various network nodes and elements (NEs), fault detection, and localization in network equipment using traditional methods was a challenging and time-consuming task. ​We partnered with them to build a holistic, scalable AI/ML-based product which included the following features:​

    • Data collection and ingestion at a large scale from a variety of heterogeneous network elements (NEs)​.
    • ML Models to determine the health of the NEs and to provide correlation with configuration changes​.
    • A mechanism to intuitively visualize and identify the nodes at fault and help them take proactive action​.
  • Inventory optimization

    For a large Indian conglomerate facing challenges such as inaccurate demand forecasts, surplus inventory levels, and poor current customer delivery lead time, we created three models: Demand forecasting, lead time prediction, and inventory optimization model. Demand forecasting helped in large scale accurate forecasting. Improvement of forecast accuracy reduced bull whip effect in the supply chain and significantly reduced the planning time. The lead-time prediction used simulation techniques to estimate lead time accurately to delivery points. This resulted in setting better customer expectations for product delivery. The inventory management module managed inventory planning and control and was built on the Reinforcement Learning framework. It was able to manage stocks with optimum inventory coverage avoiding surplus and shortage of inventory. This resulted in better utilization of working capital and a reduction of the cash conversion cycle.

    Inventory optimization | Nagarro
  • Inventory optimization | Nagarro

    For a large Indian conglomerate facing challenges such as inaccurate demand forecasts, surplus inventory levels, and poor current customer delivery lead time, we created three models: Demand forecasting, lead time prediction, and inventory optimization model. Demand forecasting helped in large scale accurate forecasting. Improvement of forecast accuracy reduced bull whip effect in the supply chain and significantly reduced the planning time. The lead-time prediction used simulation techniques to estimate lead time accurately to delivery points. This resulted in setting better customer expectations for product delivery. The inventory management module managed inventory planning and control and was built on the Reinforcement Learning framework. It was able to manage stocks with optimum inventory coverage avoiding surplus and shortage of inventory. This resulted in better utilization of working capital and a reduction of the cash conversion cycle.

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