Machine Learning - how it's Applied to MADISON

At the forefront of innovation, our pioneer product MADISON is an intelligent, data-driven marvel that transforms raw information into actionable insights. Our main product is cutting-edge and powered by advanced machine learning algorithms. MADISON sifts through vast datasets with remarkable precision, unveiling contextual understanding, structural, semantic correlations that were once hidden in data labyrinths. Its adaptive nature ensures continuous learning, refining its data understanding as new data flows in.??

MADISON's professional capabilities encompass the ability to conduct queries across various text formats from diverse sources such as PDFs, MS Documents, PowerPoint presentations, HTML, XML, pictures (Scanned PDFs). Additionally, it can handle IoT data in both textual and tabular structure, in Excel and CSV formats. Madison's orchestrator excels in direct user input to the corresponding optimized model, showcasing its versatile and advanced functionalities.??

MADISON's standout features encompass, but are not limited to, the ability to query text both in extractive and generative manner, offering insights from the original documents, querying structured data, facilitating data visualization queries, and populating transaction documents seamlessly based on natural conversations. MADISON models are designed to handle data sourced from different origins. The data is meticulously curated into a format suitable for feeding into the distinct machine learning models.?

In the context of Extractive Question-Answering Inference models, MADISON is equipped with the advantage of employing both pretrained, fine-tuned models and proprietary, in-house models tailored to client-specific data. To enhance the model's performance, Contextere has integrated modules designed to ascertain the contextual significance of plain text. The context of text is captured through various methodologies and techniques, including preprocessing, which involves cleaning the raw text; merging paragraph headers by scanning the table of contents (TOC); and transforming tables into coherent paragraphs. Moreover, the model enriches the contextual information by incorporating external sources such as ontologies, client data specific dictionaries, and databases. These processes significantly enhance the data representation for the NLP model. In addition, the model is capable of extracting both figures and tables in response to user queries.?

Generative Question-Answering inference model is designed with an effective prompt. The prompt involves clarity and specificity to enhance understanding of the user query. In accordance with our distinct model feature prerequisites, the prompt is meticulously crafted to facilitate generative responses. For question-answering tasks involving plain text, tables, and figures, the model generates comprehensive answers. Additionally, the data insight feature conducts queries on structured data by transforming natural language queries into SQL queries. Data selection is achieved through intelligently engineered prompts, and the SQL results are adequately converted into conversational responses.??

Refer to Diagram A.