Retrieval-augmented generation (RAG) is a technique that provides generative AI large language models (LLMs) with additional data so they can deliver more relevant and useful results. As business users deepen their use of generative AI for solving real problems related to their organizatons, they often find themselves frustrated by incorrect or irrelevant answers returned by an LLM. RAG can minimize these frustrations by improving the overall quality and relevance of generative AI results by incorporating information from the organization's internal knowledge bases, document repositories and other sources.

FourthLock pipelines are easy to install on the hardware of your choosing. Simply provision resources on servers that you manage on-premise or through the cloud provider of your choice. We provide the deployment deployments assets needed to depoy and manage your pipeline in a scalable Kubernetes cluster.

Our unified permission gateway tracks data lineage for all data sources and ensures that stringent security and governance controls are enforced at all pipeline stages. All output data formats include lineage tagging for enforcement of access controls. It's also important to note that since your entire FourthLock pipeline is deployed on your hardware, your data is never exposed to external services unless you choose to enable external access. FourthLock pipelines can be deployed can managed on fully air-gapped architectures.

FourthLock pipelines are structured to handle multiple inputs of batch and streaming data an can output to multiple targets to support AI applications, analytics other operational needs. Most organizations start with one pipeline for multiple purposes within a given network topology. Additional pipelines can always be added based on the needs of each operation as customers scale their usage.