AI Mail Tracker and Scheduler

Inspiration

-In today's fast-paced digital world, businesses receive a large volume of emails on a daily basis. -These emails can vary greatly in content and purpose, ranging from important communication, promotions, requests, thank you messages, feedback, reviews, job or task requests, and even error messages from customers. -Managing and responding to these emails in a timely and efficient manner can be challenging, as it requires manual sorting, categorization, and response generation by human staff. -This process can be time-consuming, prone to errors, and may result in delays in addressing customer needs or job/task prioritization.

What it does

Currently, many businesses rely on manual methods to categorize and respond to emails, which can be inefficient and time-consuming. Human staff must read through each email, determine the emotion or category of the email, and craft appropriate responses. Additionally, job or task requests in emails are often prioritized based on subjective judgment, which may not always be optimal. This manual approach can result in delays in response times, inconsistent responses, and may not be scalable for handling a large volume of emails.

Moreover, error messages from customers or technical issues reported in emails require human intervention to understand and provide solutions. This can further delay issue resolution and may not be scalable, especially during peak times when a large number of error messages are received.

Overall, the existing scenario lacks efficiency, consistency, and scalability in handling incoming emails, categorizing them, prioritizing job/task requests, and providing timely and accurate responses, especially for technical issues.

How we built it

Using NLP techniques, emotion recognition, topic modeling and generative models to automatically categorize the emails. For task based emails, a generative model to identify the priority of a task in an email and scheduling the task directly with agile project management tools. For other review related emails, a suitable response is tailored with our generative model and wait for the approval by a human agent.

Challenges we ran into

Emotion Recognition Accuracy: Emotion recognition from text can be challenging, as it may depend on various factors such as language nuances, context, and tone. The accuracy of the emotion recognition model may not always be 100%, resulting in misclassification of emails into incorrect emotions, which can lead to inappropriate or irrelevant template responses. Template Response Customization: Template responses generated by the system may not always perfectly match the specific context or tone of the email, requiring additional customization. Creating highly personalized and contextually relevant template responses can be complex and may require continuous monitoring and improvement to ensure customer satisfaction. Task/Job Request Identification: Accurately identifying emails related to job or task requests can be challenging, as they may not always be explicitly mentioned in the subject line or body of the email. The system may need to rely on pattern recognition and context understanding to accurately prioritize and categorize job/task-related emails. Generative Model Accuracy: The accuracy and effectiveness of the generative model in automatically generating responses for technical issues or error messages may vary depending on the complexity of the issues and the level of customization required. The system may need continuous training and refinement to ensure accurate and relevant responses.

Accomplishments that we're proud of

Pre-trained language models, such as BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer), have shown promising results in various natural language processing (NLP) tasks, including emotion recognition. These models are trained on large amounts of text data and can capture contextualized representations of words and phrases. And using these high efficient models for training such deep real time solutions is what we are proud of

What we learned

As we propose this project on emotion recognition, email categorization, and generative responses, we aim to gain valuable insights into the strengths, limitations, and practical implementation aspects of using automated techniques for managing emails. We will focus on understanding the effectiveness of emotion recognition models, email categorization, and prioritization using LSTM-based topic modeling, generative models for automated responses, user feedback and evaluation, scalability and practical implementation challenges, and the potential business impact and value. These learnings will help us in refining and optimizing the system for practical use in a real-world business setting.

Built With

• genmail
• ghatgpt
• python