Dataworkz simplifies RAG app development for businesses. You can build RAG applications using proprietary data, utilizing either public LLM APIs or privately hosted open-source foundation models. With Dataworkz's RAG builder, enterprises gain the ability to construct their AI stack - vector database, embedding model, and LLM - and connect with various sources of business data, including SaaS, databases, and more. Dataworkz addresses hallucinations by allowing Gen AI to reference its source data.
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 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 nocode 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.
Highlights
Dataworkz is the fastest way to build RAG based GenAI applications for private data using the staff and expertise you have today.
With Dataworkz RAG builder, you can bring in data from any source, structured, unstructured, or a combination of both.
The key capabilities of the RAG builder are preparing your data, composing your AI stack, and monitoring the GenAI app with an API layer for creating embeddable apps.
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You pay based on three usage dimensions that bill independently. Ingestion per page charges for each 500-word page of content you bring into the platform. Vectorization per page charges for converting each page into a format AI agents can search. Retrieval per query charges each time an agent searches that content to answer a question. Costs scale with how much data you load, process, and query. There is no upfront commitment. The more pages you ingest, vectorize, and the more queries you run, the more you pay.
Top-of-mind questions for buyers
How is a page counted for ingestion and vectorization billing?
A page equals 500 words of content. The platform divides your source material into 500-word segments. Each segment counts as one page for ingestion when loaded, and again as one page for vectorization when converted into a searchable format. Longer documents produce more pages and more charges.
Does my source data get charged twice if it stays in my own systems?
The platform reads source data from the systems where it already lives through your connectors. You pay ingestion per page when content enters the platform and vectorization per page when it is converted. Source databases and document stores remain in place, so storage of original data is not a billed dimension here.
Which of the three dimensions usually drives the largest share of my bill?
The three dimensions bill independently and appear on the same invoice. Ingestion and vectorization scale with how much content you load, so they dominate during initial data onboarding. Retrieval per query scales with ongoing agent activity, so it dominates once content is loaded and agents answer many questions daily.
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