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 actual usage across three separate activities. Ingestion charges per page, with each page counted as 500 words. Vectorization charges per page when your data is converted for AI retrieval. Retrieval charges per query when the platform answers a request. These dimensions are independent, not tiers. You are billed only for what you use in each category, so your cost scales with how much data you load, process, and query.
Top-of-mind questions for buyers
What counts as one page for ingestion and vectorization billing?
One page equals 500 words of source content. Ingestion charges apply when you load a page into the platform. Vectorization charges apply separately when that page is converted into a format the AI can retrieve. A document is billed by its total word count divided into 500-word pages.
Which activity drives most of my cost — ingestion, vectorization, or retrieval?
All three bill independently and appear on the same invoice. Ingestion and vectorization scale with how much data you load and prepare, so they dominate during setup or large data refreshes. Retrieval scales with query volume, so it dominates once your agents run in production and answer many requests.
Am I charged again when my data refreshes or when I re-run pipelines?
Charges apply per activity. Reloading or refreshing data triggers new ingestion charges, and re-preparing that data triggers new vectorization charges. Each query the platform answers triggers a retrieval charge. You are billed only for the activities you actually run, so repeated processing adds to your usage.
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