Centralizes access control across all systems based on a zero-trust approach and minimizes service interruptions while maximizing revenue from industrial assets. Implemented using Google Cloud and Fortinet technologies for secure and efficient management of renewable energy infrastructure.

Struggled with costly, difficult-to-manage operational technology communications solutions. Needed secure management solutions for complex information and operational technology environments.

1.0 month decrease in site commissioning time

N/A

Empowers customers to streamline product development and ensure compliance through AI-driven insights. Implemented using Google Cloud's AI technologies, including Vertex AI.

Automating the extraction of information from complex documents. Streamlining the integration of new information by automating data mapping. Ensuring consistency on packaging and labels. Verifying compliance with ever-changing regulations.

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Automated content classification enhances user engagement and operational efficiency. Implemented using Google Generative AI solutions like Vertex AI and Gemini.

The demand for tailored and engaging content is rapidly increasing. Manual content tagging was time-consuming and subjective, leading to inconsistent results.

50.0 % increase in productivity, 95.0 % decrease in misclassification, 40.0 % increase in user engagement

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Developed a Learning Experience Platform that supports over one million users and enhances learning efficiency by 10x using AI-powered services. Implemented on Google Cloud with a scalable architecture and AI technologies such as Vertex AI and Gemini.

Existing learning management systems could not support the teaching of complex cognitive skills. High latency and crashes during periods of high demand affected sales and reputation. Needed a solution to scale to one million active users.

288.0 x increase in feedback speed, 200.0 x increase in user capacity, 78.0 % decrease in CPU utilization

1000000.0 # active users

Migrated to Google Cloud to enhance content creation and achieve scalability and agility. Implemented a cloud-based infrastructure using Google Cloud services including Vertex AI and BigQuery.

Encountered technology challenges that hindered scale and performance. Scattered infrastructure technology impacted ability to release product features. Managing multiple, disconnected learning platforms made it difficult to see overall business metrics.

20.0 % decrease in ML infrastructure cost, 30.0 % increase in click-through rates, 8.0 % decrease in overall churn

99.0 % service availability on ML API endpoints, 99.9 % success rate in deploying ML pipelines

Adya's ATMA platform enables ecommerce businesses to launch apps quickly and efficiently. It automates the app development process using Google Cloud technologies like Terraform and Google Kubernetes Engine.

Faced development roadblocks in launching applications. Needed to automate the scripting process to reduce app launch time.

5.0 none decrease in app launch time, 10.0 x decrease in cost for end user

1.0 hour app launch time

Developed an AI Agent powered by Gemini Pro 1.5 to interact with SAP ERP, enabling employees to extract real-time insights using natural language queries.

Untangling data silos. Manual processes hindered agility and decision-making. Need for real-time insights from backend systems.

1.0 day decrease in query processing time

N/A

Orq.ai provides a no-code platform that enables teams to build and deploy generative AI solutions efficiently. It leverages Google Cloud's managed services for scalability and resource management.

Faced challenges in making AI accessible for product, engineering, and non-technical teams. Needed a solution to test different AI models and manage resource consumption effectively.

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1.0 hour time to start using the platform

The use case optimizes the editorial strategy and publication processes by leveraging Google Cloud services. It is implemented through the outsourcing of digital front office operations and the use of AI for article categorization and recommendations.

Optimize operations and support digital strategy. Reduce manual tasks and improve responsiveness of journalists. Standardize publication processes to enhance efficiency.

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The use case automates recruitment processes and enhances candidate matching through AI algorithms. It is implemented using Google Cloud's managed services, including BigQuery and Vertex AI.

Simplifying the recruitment and employee management process. Freeing up HR departments from time-consuming administrative tasks. Optimizing applicant matching with recruiters using smart algorithms.

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30000000.0 EUR turnover in 2021, 60000000.0 EUR turnover in 2022

Utilizes AI to analyze customer interactions and provide personalized recommendations. Implemented using machine learning algorithms and natural language processing.

Need to enhance customer engagement. Aim to improve sales conversion rates. Strive to reduce customer response time.

25.0 % increase in sales conversion rate, 50.0 % decrease in customer response time

90.0 % customer engagement score

ContactPigeon built a customer data platform (CDP) to unify customer data and enable real-time insights for retailers. It leverages Google Cloud's BigQuery for data analysis and Looker for visualization.

Retailers face challenges with siloed data. Custom reports take too long to produce. Need for real-time insights and effective customer engagement.

5.0 % increase in retailer revenue, 3.0 days decrease in report generation time

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The use case implements a near real-time query processing system to significantly reduce data retrieval times. It utilizes advanced AI technologies and cloud computing frameworks to optimize data queries.

The company aims to enhance query processing efficiency. They are addressing the challenge of slow data retrieval times.

1.0 day decrease in query processing time

N/A

The use case implements a near real-time query processing system that drastically reduces the time taken for data queries. It utilizes advanced AI technologies and cloud computing frameworks to achieve this efficiency.

The company aims to enhance query processing efficiency. The goal is to reduce the time taken for data queries significantly.

1.0 day decrease in query processing time

N/A

The use case implements a near real-time query processing system that drastically reduces the time taken for data queries. It utilizes advanced AI technologies and cloud services to optimize data retrieval processes.

The company aims to enhance query processing speed. The goal is to reduce the time taken for data queries significantly.

1.0 day decrease in query processing time

N/A

Studiosus migrated to Google Cloud to enable real-time reservations and improve customer satisfaction. The implementation involved creating a new booking portal and integrating SAP workloads on Google Cloud.

Studiosus needed to modernize its 40-year-old on-premise system to improve scalability and enable real-time bookings. The existing infrastructure was unable to handle demand peaks, affecting customer satisfaction and service.

40.0 % increase in conversion rate

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MLB leverages Google Cloud AI to analyze vast amounts of baseball data in real time, providing fans with deeper insights and contextualized statistics. This is implemented through the use of Google Cloud's APIs and machine learning capabilities.

Enhance fan engagement through real-time insights. Improve the contextualization of game statistics. Integrate technology to enrich the fan experience.

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3.5 million # clips highlight clips accessible in real time

NotCo's Conversational AI chatbot provides real-time insights from SAP data for faster decision-making. It is implemented using Google Cloud's BigQuery and Cortex Framework.

Significant challenges in harnessing SAP data. Existing setup was cumbersome and costly. Delays in decision-making and limited access to insights.

14.0 days decrease in analytics model deployment time

N/A

The use case implements AI technologies to optimize query processing, achieving near real-time results. It utilizes advanced machine learning algorithms and cloud computing resources to enhance performance.

The company aims to enhance query processing efficiency. The goal is to reduce the time taken for data queries significantly.

1.0 day decrease in query processing time

N/A

Implemented cloud-based development environments and generative AI to streamline development processes. Utilized Google Cloud's Cloud Workstations and Gemini Code Assist for efficient application development.

Development team faced scaling challenges due to complex computer simulations. Bottleneck in development process due to time-consuming tasks and steep learning curves with multiple programming languages.

6.0 months decrease in development environment setup time

N/A

Enhances ad targeting accuracy and content creation productivity through advanced data analytics and generative AI tools. Implemented by migrating to Google Cloud and utilizing BigQuery, Google Analytics 4, and Gemini API.

High costs of data analytics. Insufficient analytics performance. Need for accurate ad targeting and content delivery.

50.0 % decrease in data analytics costs, 10.0 x increase in data analytics performance

5.0 x data analyzed, 1.0 none ad targeting accuracy

Developed an AI travel agent app to assist users in planning and booking complex travel itineraries. Implemented using Elemental Cognition platform powered by Google Cloud, integrating AI models and reasoning engines for accurate itinerary generation.

Need for 100% accuracy in travel planning. Difficulty in booking complex itineraries with multiple stages. Barriers to scaling ticket sales due to technology limitations.

5.0 x increase in booking conversions

100.0 % accuracy in travel planning

The use case improves the analysis of unstructured financial data for enhanced market insights and investment advice by integrating Google Cloud Vertex AI and PaLM 2 language model into its platform.

Enhance market insights and investment advice. Analyze unstructured financial data for better decision-making.

10.0 x increase in response speed compared to competitors

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Upskilled the bank’s workforce in AI and ML through a partnership with Google Cloud. Implemented training programs and practical machine learning projects guided by Google engineers.

Lacked expertise and methodology to leverage text data effectively. Workforce lacked skill set in data science, AI, or machine learning. Hindered ability to translate customer insights into informed business decisions.

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Mystifly developed an AI-powered chatbot, Mystic, to empower users to self-serve and reduce support requests. It is implemented using Google Cloud's Conversational and Generative AI solutions, particularly Vertex AI Agent Builder, to understand user queries and provide instant responses.

Streamlining the configuration process for the Smart Selling Platform (SSP) Manager. Reducing the reliance on support agents for basic queries. Improving the onboarding experience for new users unfamiliar with airline terminology.

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Federal Bank enhances customer service through a generative AI chatbot, Feddy, powered by Google Cloud's Vertex AI and Dialogflow, enabling 24/7 multilingual support and automated processes.

Improve existing Virtual Assistant capabilities. Modernize infrastructure for better integration with fintech partners. Provide customers with seamless access to information.

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VEED utilizes Google Cloud services to build a reliable, low-latency video editing platform, enabling rapid feature deployment and efficient resource management through managed services like Cloud Run and Vertex AI.

VEED needed to keep its team lean to focus on building a world-class video editing platform. The existing video editing tools were overly complex and inaccessible to new content creators.

50.0 % decrease in storage costs, 4.05 petabytes increase in data processed monthly

12000000.0 # monthly users, 35000000.0 dollars annual recurring revenue

The use case implements a near real-time query processing system that drastically reduces the time taken for data queries. It utilizes advanced AI technologies to optimize and streamline data retrieval processes.

The company aims to enhance query processing efficiency. The goal is to reduce the time taken for data queries significantly.

1.0 day decrease in query processing time

N/A

eBay enhances its AI-driven recommendation engine using Google Cloud's Vertex AI and Vector Search to streamline product discovery and improve user experience. The implementation involves integrating cloud-based AI capabilities with existing ML workflows for faster iterations and better recommendations.

Accelerate engineering velocity for AI-driven recommendation engine. Improve user experience for product discovery. Reduce onboarding time for new ML models.

2.0 weeks decrease in onboarding time for new ML models, 4.0 ms decrease in real-time read latency

95.0 % real-time read latency achievement

The use case implements AI to optimize query processing, significantly reducing the time taken for data retrieval. It utilizes advanced machine learning algorithms to analyze and streamline data queries.

The company aims to enhance query processing efficiency. They are addressing the challenge of slow data retrieval times.

1.0 day decrease in query processing time

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Modernized data systems to secure archaeological data and make it accessible for research. Implemented Google Cloud and NetApp Cloud Volumes ONTAP for data management and backup.

Data was sitting in silos across on-premise systems. A lot of it wasn't backed up, meaning it could get lost. There was no disaster recovery plan.

1.5 month decrease in time to build data backup and disaster recovery plan, 8.0 hours decrease in recovery time objective (RTO) for critical systems

1.0 petabyte archaeological data secured, 700.0 TB data transferred to Cloud Volumes ONTAP