Unified data analytics
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Flora Food Group deployed Microsoft Fabric to consolidate multiple analytics tools into a single, unified platform, streamlining data management, reporting, and analytics under one architecture.
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Improved cost efficiency, enhanced reporting capabilities, and more detailed insights to customers
28
AI use cases in
Consumer Goods
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Mercari integrated OpenAI’s API with a multi-model approach to optimize product listings. Initially, GPT‑4 analyzed top listings offline while GPT‑3.5 Turbo provided real-time suggestions for active listings. Later, they shifted to GPT‑4o mini to automatically generate complete titles, descriptions, and category suggestions from uploaded photos, streamlining the seller listing workflow.
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ASOS integrated Azure OpenAI Service and Azure AI prompt flow to build an AI-powered natural language interface on its website and mobile app for personalized product recommendations. They implemented the solution by connecting their existing microservices with these AI tools, streamlining rapid prototyping and integrating external trend data along with internal expertise to curate tailored selections that enhance customer engagement. The solution seamlessly integrates into ASOS’s digital processes.
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Pets at Home built an AI agent using Microsoft Copilot Studio integrated into its unified Azure data platform that consolidates disparate systems from its retail stores, online channel, veterinary clinics, and grooming services. This agent empowers the retail fraud detection team by rapidly scanning extensive transaction data to identify anomalies such as duplicate images in fraudulent claims, thereby streamlining fraud investigations. The implementation required minimal coding and seamlessly connected existing systems while ensuring strict data privacy within the company ecosystem.
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151
companies using
Data Agents
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wealthAPI implemented a next‐gen contract detection solution by integrating DataStax Astra DB on Google Cloud and leveraging Google Gemini models for AI‐powered analysis. They deployed DataStax’s vector search and real‐time insights capabilities to scale contract detection across millions of users in less than three months, streamlining wealth management workflows by dramatically reducing response times and efficiently handling massive data volumes.
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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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251
solutions powered by
Microsoft
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Physics Wallah developed 'Gyan Guru', a hyperpersonalized conversational study companion to address the unique academic and support needs of its 2 million daily users. The system was implemented by indexing over one million Q&As and ten million solved doubts in a vector database, then leveraging a Retrieval-Augmented Generation (RAG) approach integrated with Azure OpenAI to deliver individualized, context-aware responses. This integration streamlined various student interactions including academic queries, product-related issues, and general support, reducing reliance on human subject matter experts.
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Brandix adopted the Microsoft 365 Copilot suite via the Early Access Program to integrate AI into key executive workflows, automating meeting transcript-based action tracking in Teams, accelerating pitch deck creation in PowerPoint, and streamlining document generation in Word while leveraging AI-enhanced querying in Power BI and efficient information retrieval via Copilot Chat. This implementation transformed internal communications, reporting, and presentation preparation processes by significantly reducing manual effort.
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University of Oxford’s IT Services department deployed Microsoft 365 Copilot across its 400+ staff as part of a pilot program aimed at embedding generative AI into research, teaching, and administrative workflows. They implemented extensive onboarding sessions, community engagement via Microsoft Teams, and established an acceleration team to integrate Copilot with other business applications. This deployment streamlined routine tasks such as document generation, summarization, and meeting support, fostering innovation within the department.
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159
AI use cases in
Europe
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Deutsche Bank developed DB Lumina, an AI-powered research agent built on Gemini and Vertex AI through a partnership with Google Cloud. The solution automates the creation of financial research reports by rapidly condensing extensive market data—such as converting a 400-page report into a three-page summary—thereby streamlining analysis workflows while maintaining rigorous data privacy standards.
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Capgemini partnered with Google Cloud to develop industry-specific agentic AI solutions that automate customer request handling across multiple channels such as web, social, and phone. The implementation integrates Google Agentspace, Customer Engagement Suite, and Agent2Agent interoperability protocol into existing customer service infrastructures to enhance personalized support, call routing, and workflow automation. This advanced solution transforms customer experience by streamlining communications and enabling proactive engagement.
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wealthAPI implemented a next‐gen contract detection solution by integrating DataStax Astra DB on Google Cloud and leveraging Google Gemini models for AI‐powered analysis. They deployed DataStax’s vector search and real‐time insights capabilities to scale contract detection across millions of users in less than three months, streamlining wealth management workflows by dramatically reducing response times and efficiently handling massive data volumes.
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