At the heart of our mission is the intelligent integration of generative AI into your business environment. We support your digital transformation by deploying AI solutions tailored to your daily challenges, with a structured approach around three key areas.
Our process begins with a thorough analysis of your existing cloud infrastructure (Azure, AWS, Google Cloud). We then evaluate your workflows to identify generative AI automation opportunities, determining the most suitable models (GPT-4, Claude, open-source on-premise solutions). Finally, we establish a detailed roadmap covering cloud architecture, security, and costs.
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For precise use cases such as information extraction and analysis from your documents and knowledge base creation, the integration includes the implementation of robust APIs, deployment on your existing infrastructures, and connection with your internal systems, as well as the creation of dedicated documentation.
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Our services are modular and customizable according to your specific needs, your level of maturity in AI, and your existing cloud infrastructure. Our goal is to continuously support you towards a successful adoption of generative AI, maximizing return on investment (ROI) while minimizing risks
Our experts develop customized solutions using the latest cloud technologies, creating specific datasets for your sector and optimizing models for your needs. The integration includes implementing robust MLOps pipelines, monitoring systems, and storage solutions. We orchestrate workloads via Kubernetes or serverless services, ensuring seamless connection with your internal systems.
We offer training programs tailored to each profile: technical sessions for developers on generative AI APIs, training for cloud architects on model deployment, and awareness for managers on AI issues. Our workshops and guides also cover AI ethics, governance, security, and data confidentiality in the cloud.
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The main cloud platforms for generative AI include Microsoft Azure with Azure OpenAI Service and GPT-4, Amazon Web Services (AWS) with Claude on Bedrock, and Google Cloud. Each platform offers solutions tailored to different data processing and automation needs.
The audit begins with a thorough analysis of your existing cloud infrastructure, evaluates your current workflows, and identifies automation opportunities. This assessment considers the volumes of Big Data to be processed and results in a strategic roadmap detailing the necessary cloud architecture, security, and costs.
Data security is ensured through the implementation of secure storage solutions, robust MLOps pipelines, and advanced monitoring systems. The use of “Secure GPT” and adherence to industry standards for APIs ensure the protection of your sensitive information.
Implementation requires suitable cloud infrastructure, robust data pipelines, and a distributed architecture capable of supporting AI workloads. Systems must be compatible with modern frameworks and allow scalability via Kubernetes or serverless services.
Return on investment is assessed through workflow optimization, reduction of operational costs, improvement of team productivity, and automation of repetitive tasks. A continuous monitoring system allows tracking of these performance indicators.
Successful integration involves a gradual approach including the creation of robust APIs, deployment on existing infrastructures, and seamless connection with internal systems. Detailed documentation and ongoing support ensure a smooth transition and successful adoption by teams.
