Reporting & Analysis Automation with AI Chatbots

We developed a multi-layered system for user request classification using LLMs. The first layer directs queries to the Q&A (Question Answering) system or a user request processing module. The second layer refines categorization based on predefined criteria from a customizable configuration file. Finally, the NER (Named Entity Recognition) system extracts key parameters, streamlining request handling or prompting for more details as needed. This solution ensures that each query is handled by the appropriate branch, delivering more accurate routing and handling of requests.

Using the RAG (Retrieval-Augmented Generation) approach, we developed a Q&A system to combine document retrieval with generative answering for accurate, context-rich responses. Essential data was preloaded to boost efficiency and user experience, streamlining response generation. We integrated the Langchain LLM library to manage complex queries efficiently, ensuring excellent system performance.

We gather information tailored to user requests and perform statistical analysis using the Prophet library to analyze data sequences collected over time. By leveraging the Langchain LLM library for prompt engineering, we provide users with accurate answers and deliver valuable insights.

We have identified several problem categories. For each category, there is a specific solution. Using analysis methods, we determine the nature of each problem and provide the user with the appropriate solution.

We engineered a high-performance architecture featuring AI agents and LLMs to deliver swift responses and maximize system efficiency. This innovative setup organizes user request processing into multiple threads, ensuring rapid and seamless performance:

• Implemented a multi-layered user request classification system using LLMs: the first layer directs queries to Q&A or processing modules; the second layer refines categorization based on predefined criteria from a configuration file; NER extracts parameters, optimizing request handling.
• For data analysis and trend analysis requests, Dataforest has implemented a strategy to reduce processing time. By splitting the task into three components (table generation, reasoning part, and status reporting) and running them in parallel, we have significantly improved the overall response time, enhancing system efficiency.