At Gruve, we understand that successful AI implementation requires a robust and scalable AI infrastructure. A well-architected infrastructure powers your AI models, ensuring they run efficiently, scale effortlessly, and deliver consistent, reliable results. Whether you’re deploying machine learning models, managing big data, or building AI-driven applications, our comprehensive AI infrastructure services provide the foundation for your enterprise AI solutions. With expertise in cloud computing, high-performance computing (HPC), and data pipelines, we help businesses maximize the value of their AI investments.
We design and implement native or custom AI assistants, powered by LLMs and trained on your internal data. We make sure the AI assistants understand your domain-specific queries and continuously learn from users to enhance their performance. We train your team to enable AI assistants in their day to day workflow.
We offer data pipeline optimization services that ensure seamless data flow for AI training and inference. Our pipelines manage large datasets required for machine learning models, streamlining ETL processes for real-time data collection and processing. This infrastructure supports continuous model improvement, enabling your AI models to access up-to-date data from multiple sources in an efficient manner.
We help ensure that your machine learning models operate efficiently in real-time applications. We help you design infrastructure for low-latency AI inference, enabling applications such as virtual assistants and content generation tools to respond quickly and accurately. With optimized resource allocation, we help reduce costs while maintaining high performance and availability.
We partnered with a leading global satellite imagery company to tackle scalability issues in processing large volumes of imagery with AI models. Their slow data pipelines delayed insights for customers. We deployed a cloud-based infrastructure using AWS to optimize GPU/TPU instances for real-time processing. By implementing NVIDIA GPU-based HPC solutions, we reduced model training times by 62%, and integrated Apache Kafka for streamlined data ingestion. This approach resulted in a 37% cost savings in cloud infrastructure through efficient resource utilization.
