Together AI: Advancing the Frontier of AI With Open Source Embeddings, Inference, and MongoDB Atlas

Together AI is a research-driven artificial intelligence company that aims to create the fastest cloud platform for building and running generative AI (gen AI). Founded in San Francisco, it has raised over $120 million from investors such as Nvidia, Kleiner Perkins, Lux, and NEA. The company's mission is to advance the frontier of AI with open-source research, models, and datasets while promoting transparency and innovation. Together AI recently introduced its Together Embeddings endpoint, a new service for developers building applications like retrieval-augmented generation (RAG). This enables gen AI models to be fed with domain-specific data, resulting in more reliable outputs customized for businesses and reduced risks of hallucinations. The Together Embeddings endpoint offers access to eight leading open-source embedding models at up to 12x cheaper price than proprietary alternatives. The company has also integrated its services with MongoDB Atlas, LangChain, and LlamaIndex for RAG applications. To demonstrate this integration, the engineering team at Together AI created a tutorial for developers exploring how to build a RAG application with MongoDB Atlas. This tutorial shows how to use Together Embeddings and Together Inference to generate embeddings and language responses while using Atlas Vector Search to store and index embeddings and perform semantic search for natural language queries against a sample Airbnb listing dataset. The introduction of resource tagging for projects marks an improvement in how users can categorize, organize, and track projects within MongoDB Atlas, streamlining cloud resource management. This enhancement enables users to apply resource tags to projects, further enriching the way they can associate metadata with their cloud resources. Credit scoring plays a pivotal role in determining who gets access to credit and on what terms. Despite its importance, traditional credit scoring systems have long been plagued by issues such as biases, discrimination, limited data consideration, and scalability challenges. To overcome this, banks and other lenders are looking to adopt artificial intelligence (AI) to develop increasingly sophisticated models for scoring credit risk. Alternative credit scoring refers to the use of non-traditional data sources and methods to assess an individual's creditworthiness. While traditional credit scoring relies heavily on credit history from major credit bureaus, alternative credit scoring incorporates a broader range of factors to create a more comprehensive picture of a person's financial behavior. Besides the use of alternative data and AI in credit scoring, generative AI (GenAI) has the potential to revolutionize credit scoring and assessment with its ability to create synthetic data and understand intricate patterns, offering a more nuanced, adaptive, and predictive approach. GenAI’s capability to synthesize diverse data sets addresses one of the key limitations of traditional credit scoring – the reliance on historical credit data. By creating synthetic data that mirrors real-world financial behaviors, GenAI models enable a more inclusive assessment of creditworthiness. The convergence of alternative data, artificial intelligence, and generative AI is reshaping the foundations of credit scoring, marking a pivotal moment in the financial industry. The challenges of traditional models are being overcome through the adoption of alternative credit scoring methods, offering a more inclusive and nuanced assessment. Generative AI represents the forefront of innovation, not only revolutionizing technological capabilities but fundamentally redefining how credit is evaluated, fostering a new era of financial inclusivity, efficiency, and fairness.