The Squaredev Platform¶
Squaredev offers an API to add cutting-edge AI features to any system. Squaredev creates AI systems and puts them behind a simple API for anyone to use with no prior knowledge in AI. Moreover, users can create use their data to modify existing models through Retrieval Augmented Generation or enable other AI features like semantic search, recommendations audio transcription and summarization. Squaredev handles the complexities of managing AI models, collecting massive amounts of text data for AI, the ever-evolving architectures of assistants, and the infrastructure needed to build with AI.
Here are a few examples of language understanding systems that can be built on top of large language models.
Create AI assistants that can answer questions, perform tasks, and have conversations with users. Automate customer support, sales, and other business processes to create a seamless experience for your users. Add your company data to create a custom assistant that can answer questions about your products and services. Add you user data that is peroanlized to your user according to their preferences and behavior.
Power text summarization, the process of extracting the most important or relevant information from a piece of text and presenting it in a concise and coherent manner. For example, embeddings can help the model to summarize news articles, product reviews, research papers, etc. These models can be trained on large datasets of text and can learn to identify the most important information in a large piece of text.
Semantic search, which is a search technique that uses natural language processing to understand the meaning of search queries and documents. This allows for more accurate and relevant search results. LLM-enabled semantic search can be used to represent both the meaning and intent of a user's query and documents in the embedding space, allowing for fast and accurate search results tailored to the user's needs.
LLMs and embeddings can be used to power recommendation systems, which are used to suggest products, services, or content to users based on their preferences and behavior. For example, the text embedding can be used for recommendation systems as a strong feature for training recommendation models. These models can be trained on large datasets of user behavior and can learn to predict which products or services a user is likely to be interested in.
Embeddings can help assign labels or categories to texts based on their meaning and context, enabling tasks such as classifying texts as positive or negative, spam or not spam, or news or opinion