Quaeris
Based on your interests, history, and role, you will receive personalized and recommended results. QuaerisAI provides near-real-time data access for all data. QuaerisAI enhances your data and document workload with AI.
To increase knowledge sharing and track performance, teams can share insights and pinboards. Our advanced AI engine transforms your inquiry to a database-ready language within micro-seconds. Data is nothing without context, just like life. Our cognitive AI engine interprets search terms, interests, roles, and past history to provide ranks results that allow further exploration. You can easily add filters to search results to dig into the details and explore relevant questions.
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Vertex AI
Fully managed ML tools allow you to build, deploy and scale machine-learning (ML) models quickly, for any use case.
Vertex AI Workbench is natively integrated with BigQuery Dataproc and Spark. You can use BigQuery to create and execute machine-learning models in BigQuery by using standard SQL queries and spreadsheets or you can export datasets directly from BigQuery into Vertex AI Workbench to run your models there. Vertex Data Labeling can be used to create highly accurate labels for data collection.
Vertex AI Agent Builder empowers developers to design and deploy advanced generative AI applications for enterprise use. It supports both no-code and code-driven development, enabling users to create AI agents through natural language prompts or by integrating with frameworks like LangChain and LlamaIndex.
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Atomwise
Our innovative AI engine is revolutionizing the drug discovery process, enabling the creation of superior medications at an accelerated pace. The breakthroughs we achieve contribute to the development of medicines more efficiently and effectively. Our portfolio of AI-driven discoveries encompasses entirely owned and collaboratively developed pipeline assets, supported by leading investors in the industry. Atomwise has engineered a cutting-edge machine-learning discovery platform that merges the capabilities of convolutional neural networks with extensive chemical libraries to identify new small-molecule treatments. The key to transforming drug discovery through AI lies in our talented team. We are committed to enhancing our AI platform and leveraging it to revolutionize the discovery of small molecule drugs. It is essential that we confront the most daunting and seemingly insurmountable targets, streamlining the entire drug discovery process to provide developers with increased opportunities for success. Enhanced computational efficiency allows us to screen trillions of compounds virtually, significantly boosting the chances of finding viable solutions. Our impressive model accuracy has successfully addressed the persistent issue of false positives, underscoring the reliability of our approach. Ultimately, our dedication to innovation and excellence sets us apart in the quest for breakthrough therapies.
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Google Cloud Natural Language API
Leverage advanced machine learning techniques for thorough text analysis that can extract, interpret, and securely store textual data. With AutoML, you can create top-tier custom machine learning models effortlessly, without writing any code. Implement natural language understanding through the Natural Language API to enhance your applications. Utilize entity analysis to pinpoint and categorize various fields in documents, such as emails, chats, and social media interactions, followed by sentiment analysis to gauge customer feedback and derive actionable insights for product improvements and user experience. The Natural Language API, combined with speech-to-text capabilities, can also provide valuable insights from audio sources. Additionally, the Vision API enhances your capabilities with optical character recognition (OCR) for digitizing scanned documents. The Translation API further enables sentiment understanding across diverse languages. With custom entity extraction, you can identify specialized entities within your documents that may not be recognized by standard models, saving both time and resources on manual processing. Ultimately, you can train your own high-quality machine learning models to effectively classify, extract, and assess sentiment, making your analysis more targeted and efficient. This comprehensive approach ensures a robust understanding of textual and audio data, empowering businesses with deeper insights.
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