Message AI (NLP orchestration)

Message AI (NLP orchestration) is an API-based platform empowering users to train AI models for sentiment analysis, named entity recognition, and named intent classification without prior machine learning expertise.


Message AI (NLP orchestration) is a powerful product that allows users with no prior machine learning expertise to efficiently train and utilize AI models for natural language processing (NLP) tasks. With an intuitive API, users can input their data, and the system automatically introduces models for sentiment analysis, named entity recognition, and called intent classification. By empowering users to generate insights from their data and providing personalized replies, Message AI revolutionizes NLP capabilities while ensuring fairness and efficiency in processing user requests.

The Problem

Message AI addresses several problems related to machine learning and natural language processing (NLP). Here are the problems that this product can solve

Lack of Machine Learning Expertise

The lack of machine learning expertise hinders individuals from effectively utilizing AI models for NLP tasks, limiting their ability to gain insights and automate processes. Without specialized knowledge, users may struggle to train, deploy, and optimize models, impeding their ability to leverage the benefits of AI in language processing.

Complex Model Training Process

The complex model training process often challenges users without machine learning expertise, requiring substantial time, resources, and technical knowledge.

Customization and Personalization

In traditional NLP solutions, customization and personalization options are limited, making it challenging for users to adapt models to their specific needs.

How does it work

Message AI (NLP orchestration) provides a user-centric API to enter data and automatically train AI models for NLP activities. Django, Celery, BERT, and RabbitMQ handle user requests efficiently. After data submission, NLP models are prepared for sentiment analysis, named entity recognition (NER), and named intent categorization. A queue-based system ensures equitable processing and customized responses depending on data and model. Message AI simplifies AI use without machine learning skills, revolutionizing NLP activities.

User Data Input

Users interact with the Message AI API by inputting their data, such as text messages or sentences, through a user-friendly interface. The data can include various examples of the desired NLP task, with a maximum limit of 10-15 custom examples per user.

Model Training and Queuing

Once the user submits their data, the system automatically trains the appropriate AI models based on the specific NLP tasks required: sentiment analysis, named entity recognition (NER), and named intent classification. To ensure fairness and efficiency, a queue-based system is implemented. User requests are placed in a queue, and models are trained and processed in the received order.

Inference and Customized Replies

The system performs inference on the user’s data after training the models. The trained models analyze the input text for each user and generate insights or predictions based on the NLP task. The replies are then customized and tailored to each user’s data and the specific model they chose for training. This personalized approach ensures that users receive relevant and accurate information based on their requirements.

The Solution

Message AI provides a comprehensive set of solutions to address the challenges of training AI models for natural language processing (NLP) tasks. With its user-centric API, individuals with no machine learning expertise can effortlessly train models by inputting their data. The automated model training feature streamlines the process, saving users valuable time and effort. By implementing a queue-based system, Message AI ensures fairness in processing user requests, while customized replies based on user data and selected models provide personalized insights. With these solutions, Message AI revolutionizes NLP capabilities, empowering users and delivering efficient, tailored results.

User-Centric API

Message AI offers a user-centric API that enables individuals with no prior knowledge of machine learning to easily train AI models. Users can input their data into the API, and the system automatically trains models based on that data. This solution empowers users to leverage AI capabilities without the need for expertise in machine learning.

Automated Model Training

The product automates the process of model training based on user-provided data. Users can input their data, and the system handles the training process, eliminating the need for manual model development. This solution saves time and effort for users, allowing them to quickly generate models for sentiment analysis, named entity recognition, and named intent classification.

Queue-Based System

To efficiently handle user requests, Message AI implements a queue-based system. User requests for model training are placed in a queue and processed in the order they are submitted. This solution ensures fairness and prioritizes requests on a first-come, first-served basis. The queue-based system optimizes resource utilization and provides an organized workflow for users.

Customized Replies

Message AI generates customized replies based on user data and the selected model for training. Users receive personalized responses tailored to their specific data, enhancing the relevance and usefulness of the generated insights. This solution enables users to gain actionable information specific to their context, improving their decision-making process.



The Challenges

Scalability and Performance Optimization:

Message AI implementation requires scalability and performance optimization. The system must efficiently manage more users and model training as it expands. The queue-based system used to process user requests must accommodate a high volume of concurrent requests without slowing down. To respond quickly to users, the training and inference processes must be optimized as model complexity and data grow.

User Education and Support

Message AI also struggles to educate and support non-machine learning users. Users can train AI models on their own, but comprehending input requirements, model selection, and results may take time. Documentation, tutorials, and perhaps interactive guides may be needed to help users use the system. The Message AI platform should also include a solid customer support system to solve any questions, complaints, or problems consumers may have.


Customer Support Automation

Message AI can be integrated into customer support systems to automate responses and provide quick, accurate assistance to users. By training the models on relevant data, the system can understand and respond to customer queries, reducing the need for manual intervention and improving response times.

Market Research and Analysis

The sentiment analysis model of Message AI can be utilized in market research and analysis to gauge public opinion and sentiment towards products, services, or brands. Companies can extract valuable insights from social media data, reviews, and customer feedback, enabling them to make data-driven decisions and tailor their strategies accordingly.

Chatbot Development

With Message AI, developers can train models for named intent classification, allowing them to build intelligent chatbots. These chatbots can understand user queries, classify their intents, and provide appropriate responses. This improves user engagement, streamlines customer interactions, and enhances the overall user experience.

Content Moderation

Message AI’s models can be employed for content moderation on platforms such as forums, discussion boards, and comment sections. By automatically detecting and filtering inappropriate or offensive content, the system helps maintain a safe and respectful online environment, reducing the burden on human moderators.

Information Extraction for Data Analysis

The named entity recognition model of Message AI enables the extraction of specific entities (such as names, locations, and organizations) from text data. This functionality is valuable for data analysis tasks, including market intelligence, trend analysis, and competitive research. By automatically extracting key information, businesses can derive actionable insights and make informed decisions.