Cloud AI integration connects managed AI services on Azure, AWS or Google Cloud to your applications and data, so you can add prediction, document processing or generative AI without building and running the infrastructure yourself.
Cloud AI platforms provide ready-made intelligence and elastic infrastructure. We integrate AWS SageMaker, Azure AI Services and Google Vertex AI into your products, and deploy custom models as secure, scalable APIs and microservices.
Leverage AWS, Azure, or Google Vertex AI for scalable AI solutions. At Prominent AI Services LLP, we deliver cloud AI integration end to end, from discovery and data preparation to deployment, monitoring and support, so your teams get dependable results quickly and securely.
Leverage AWS, Azure, or Google Vertex AI for scalable AI solutions.
Ideal for: SaaS Providers, Startups, Enterprises. We map the target process, identify where AI adds the most value, prototype the integration in a safe environment and roll out gradually with user training, monitoring and feedback loops.
We work on Microsoft Azure (Azure OpenAI, Azure Machine Learning, AI Search, Document Intelligence), AWS (Amazon Bedrock, SageMaker, Lambda) and Google Cloud (Vertex AI). Infrastructure is defined as code, containers run on managed Kubernetes or serverless services, and models are versioned and monitored with MLOps practices. Custom code is written in Python and Node.js.
Engage us for a fixed-scope integration, or hire DevOps engineers and AI developers to work inside your team. If you would rather we run the model for you, see custom AI API hosting and model as a service.
Planning to run AI in the cloud? Tell us your platform and use case, and we will propose an architecture and a cost estimate. Start your AI journey.
Common questions about our Cloud AI Integration services.
All three major clouds are strong. We recommend based on your existing cloud footprint, required AI services, data residency and cost.
Yes. We package models as containerised REST or GraphQL services with autoscaling, authentication and monitoring.
We right-size compute, use serverless and spot capacity where suitable, cache results and monitor usage with budget alerts.
There are two parts: our one-time integration work, which depends on the number of systems and services involved, and your ongoing cloud and model usage. We give a fixed quote for the integration and a monthly running-cost estimate before work starts.
Connecting one AI service to one application typically takes a few weeks. Larger programmes with several data sources, private networking and compliance reviews take two to three months.
Yes. We normally build inside your own Azure, AWS or Google Cloud subscription and follow your security policies, so you keep full ownership of the infrastructure and data.
Yes. The major cloud providers offer AI services in many regions, including India, Europe and the US. We choose services and regions that meet your data-residency requirements and flag any service that is not available locally.
