Modeling protein dynamics
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Superluminal implemented a dynamic protein modeling platform leveraging advanced computational techniques and Google Cloud's compute power. They built a predict-design-test architecture that integrates multiple protein conformations to generate candidate-ready compounds targeting GPCRs. This AI-driven solution streamlines drug discovery workflows by combining deep biology, chemistry expertise, and machine learning to accurately capture protein dynamics.
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75
AI use cases in
Healthcare
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Ontada leveraged Microsoft’s Azure OpenAI Service Batch API and Azure AI Foundry to build its ON.Genuity platform, which processes 150 million unstructured oncology documents to automatically extract nearly 100 critical data elements across 39 cancer types. They integrated the new platform with their structured iKnowMed system using Azure Databricks for data ingestion and Azure Document Intelligence for text extraction, transforming manual chart review processes into an automated workflow that supports clinical decision-making and life science product development.
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DrumBeat.ai developed an AI-driven diagnostic tool integrated with an ear camera that analyzes digital images of patients’ ears to provide instant identification of abnormalities such as perforated eardrums and signs of chronic otitis media. The system was implemented by training the model on over 10,000 images from 4,000 children across 100 indigenous communities, enabling non-specialists in remote areas to perform assessments and facilitate timely referrals.
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Dynamic Health Systems built its VitruCare365® platform on Microsoft Cloud for Healthcare by integrating Microsoft Azure, FHIR, Dynamics 365, and Azure OpenAI Service to deliver personalized patient apps and AI-powered chatbots. They deployed a retrieval augmented generation technique with custom RAG Flag technology and integrated the solution within Microsoft 365 for telemedicine consultations, appointment scheduling, and motivational care planning. This comprehensive integration automated patient engagement and care plan management, streamlining clinical workflows.
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149
companies using
Data Agents
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Aura Intelligence integrated Anthropic's Claude via Amazon Bedrock into its data pipeline to automatically classify over 200 million job titles and industry pairings from multi-language data, replacing manual lookups and fuzzy matching. They fine-tuned foundation models on proprietary datasets and leveraged AWS infrastructure, including SageMaker and prompt management, to automate QA, report generation, anomaly detection, and real-time hiring trend analysis.
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LaunchNotes leverages Claude in Amazon Bedrock in their product 'Graph' to transform engineering data into actionable insights. Graph functions as an ETL platform with Claude managing data pipelines, helping engineering managers understand development metrics, reduce incident identification time, automate updates, and generate customized release notes and technical documentation.
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Snowflake integrated Anthropic's Claude into their platform to enable natural language queries on complex databases, allowing customers to extract insights without SQL expertise while maintaining security and governance.
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255
solutions powered by
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704 Apps implemented an AI solution using Vertex AI and Gemini 1.5 Pro to automate and accelerate driver identity verification and safety monitoring. They integrated these AI models into their existing cloud infrastructure built on Firebase and Google Kubernetes Engine, centralizing real-time data for document validation and audio sentiment analysis. The system alerts the central monitoring team when risk-related language is detected, streamlining operational decision-making and enhancing security.
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OPPO integrated Google Cloud’s Vertex AI, AutoML, and Gemini large language model into its mobile devices to automate user feedback analysis, power AI Recording Summary features, and enable AI Toolbox functionalities such as AI Writer and AI Reply. They re-engineered their hardware platform, operating system, and third-party ecosystem to embed AI agents that optimize power consumption and reduce computing latency, streamlining mobile development workflows and enhancing user experience.
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Dataïads built an AI-powered “Post-Click Experience” system that automatically generates personalized landing pages by analyzing user context such as ad origin, product type, and behavior. The solution is implemented by integrating API access from Google Ads with Google Cloud managed services (Cloud Run and BigQuery) for automated scaling and controlled cost management, while planning to incorporate Vertex AI for further optimization. This implementation directly enhances ad campaign management and improves ecommerce conversion processes.
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279
AI use cases in
North America
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Zendesk integrated OpenAI's models to create adaptive AI service agents that autonomously manage customer conversations and execute resolution tasks. They implemented a multi-agent architecture featuring task identification, conversational RAG, procedure compilation, and procedure execution agents integrated with existing support workflows through API calls and natural language procedure definitions, while providing real-time chain-of-thought visibility. This solution transitions from traditional intent-based bots to a hybrid model of scripted and generative reasoning, streamlining customer service processes.
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Chatbase integrated Anthropic's Claude into its multi-channel customer support platform to automate routine inquiries and deliver instant, personalized support across web chat, WhatsApp, Slack, and Instagram. They implemented the solution by embedding AI agents through APIs that integrate with business systems such as Stripe for order status checks and refund processing, while enabling customizable brand voice and multilingual communication with optional human oversight. This approach streamlined customer service workflows and provided advanced analytics to help teams quickly identify trends and optimize interactions.
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Pensieve integrated Anthropic's Claude models (Claude 3.5 Sonnet and Claude 3.7 Sonnet) to build AI teaching assistants that automate grading and provide 24/7 personalized tutoring. The system clusters student submissions to calibrate grading rubrics and transforms static PDFs into interactive worksheets while enforcing instructor-controlled policies. This integration streamlines course management workflows and enhances real‐time classroom analytics.
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