Project requirements for apple online store. Apple support agent's they receive bulk emails on daily basis and previously they use to select all email's manually and mark them accordingly like spam, thank you email, Correct email, Promotion email..etc. The requirement is to build a model/system to avoid manual email selection and just do the robotic stuff. For the same use case there is one ML model/system is already in live. On top of ML model/system

We have developed DL model/system for future upgradation. When customer ready for email classification upgradation then we'll go with this DL model.

This requirement for apple online store (Customer system) to build a Top Trending email's by using Machine learning Supervised LDA technique to Unsupervised LDA technique (Robotic system).

Customer proposed us to build a ML model to show top trending email's based on the subject/issues. So that it will help to the support agent to find relevant emails instead of going through each and every email of subject/issue line. As per customer requirements we have builded a machine learning model where it will filter and show the top trending emails, what customers are talked about more in a single click. Goal of this requirement to improve the customer support digital experience.

Based on customer requirement we have builded a solution to capture human and count number of human passes through the respective cctv surveillance camera with help of OpenCV,PIL,dlib,math ..etc. libraries.

This solution is capable of detecting human and track their in and out from

the respective cctv surveillance camera. we have tested this solution through video files(.mp4, .avi) and also through webcam. Once a human is detected from video/webcam, we have tracked that person and assigning him/her a label as person in and out. We have used Machine learning and image processing with help of OpenCV(Computer Vision library), PIL, Dlib.

Requirement to build knowledge base bot where any user can ask FAQ related to internal and public data. To use this solution user needs to register first then user needs to login. Once user logged in successfully the data will available based on their role. Once user logged in, they can chat with our knowledge bot if they want to know related to HR FAQ, Finance FAQ, IT Support FAQ, Admin & Facility FAQ ..etc. User also can upload and train their own data. Also, we have added separate form where user can add manually question and answer. User also can search the particular FAQ is available or not. If data is not available they can add it manually. Also, we have integrated glimpz DB through glimpz service which is our internal database. User also can search any query related to glimpz. Document search functionality we have enabled, user can search any document related to internal and public. Display time when user ask time related query. Connect to public data with URL support followed by chat session management (Previous and current history). We are displaying a generic message if the data is not available in the database.