Dataworkz empowers businesses to effortlessly develop Retrieval Augmented Generation (RAG) applications using proprietary data, utilizing either public LLM APIs or privately hosted open-source foundation models.

Dataworkz's RAG builder streamlines building GenAI applications to remove the complexity associated with stitching together a turnkey solution. A composable AI stack provides the ability to choose the vector database, embedding model, chunking strategy, and LLM model. You have the flexibility to use public LLM APIs, including AWS Bedrock and OpenAI, or host an open-source model in a VPC.

For Advanced RAG applications, Dataworkz provides the ability to combine lexical and semantic search with metadata filtering, thereby enabling RAG apps to process large volumes of unstructured, semi-structured, or structured data.

Dataworkz connects to different sources of business data - SaaS services, relational databases, NoSQL databases, files stored in cloud object stores and provides no-code transformations to make proprietary data in any format ready for LLM applications. When combining data from multiple sources, you can also configure the precedence order for input sources used to build the context for generating LLM response.

The emergence of hallucinations presents a notable obstacle in the widespread adoption of Gen AI within enterprises. Dataworkz enables GenAI to reference its origins, consequently enhancing traceability.

RAG builder provides an API for any developer to embed GenAI applications into their existing workflow with complete flexibility to customize the look and feel.

Dataworkz empowers businesses to effortlessly build RAG applications, ensuring traceability for Gen AI while offering flexibility in building and connecting AI components with diverse sources of business data.