At Matrix, we’ve worked on more than 150 different projects, many of which include Data Science requirements. These can range from traditional Machine Learning (ML) and Deep Learning (DL) solutions to more recently Generative Artificial Intelligence (GAI) solutions. Our project methodology follows our own version of CRISP-DM which has evolved along with them, ensuring problem understanding, data wrangling, model creation and deployment along with built-in model-quality validation using standardized procedures. Our use cases include classic supervised, non-supervised and classification models, image classification, object recognition, Natural Language Processing (NLP) and graph ML models.

Obtaining the best model for any given problem requires a thorough understanding of the nature of the underlying data, considering possible bias-related corrections when applicable, and addressing interpretability issues. At Matrix, we follow an Auto-ML methodology to ensure optimal model selection and use bias-correcting procedures to adjust estimated results. We also apply various variable-importance and model explainability techniques, such as SHAP or LIME, to understand the significance of different attributes in the output.

Unlike traditional AI, which is designed to recognize patterns or make predictions, generative AI produces original outputs based on the data it has been trained on. Our applications include customized chatbots, RAGs for Smart document querying and text embeddings based on LLMs for different use cases.

Our AI-driven virtual assistants or customized chatbots are designed to understand and respond to particular user requirements, which we integrate with databases, workflows, or branding elements. They are programmed to handle unique queries, automate processes, and provide personalized interactions, making them valuable tools for enhancing customer service, streamlining operations, and engaging users in a more meaningful way.

Retrieval-Augmented Generation (RAG) models combine the strengths of retrieval-based systems and generative AI to improve the accuracy and relevance of the generated content. In a RAG model, the system first retrieves relevant information from a large database or knowledge source such as a collection of documents using embeddings based on a user’s query. Then, it uses a generative model to produce a response that incorporates the retrieved data. This approach assures accurate, up-to-date information, making RAG models especially useful for tasks requiring precise and informed responses.

Embeddings based on Large Language Models (LLMs) are techniques that convert words, sentences, or documents into dense numerical vectors that capture their meanings in a multi-dimensional space. LLMs learn these embeddings by understanding the relationships and context within large amounts of text data. The resulting vectors represent semantic similarities, meaning similar words or concepts are positioned closer together in this space. These embeddings are crucial for tasks like search, clustering, and recommendation systems, as they allow machines to process and compare textual information in a way that mirrors human understanding.