Dataworkz provides a platform for businesses to build and deploy Generative AI applications using Retrieval Augmented Generation (RAG) without the need for infrastructure management or advanced developer skills. The solution enables rapid data ingestion, transformation, and optimization, allowing teams to enhance customer experiences and improve productivity through tailored AI applications.
Funding
Funding not disclosed
Founders
Product
Problem
Building Generative AI applications with Retrieval Augmented Generation (RAG) often requires significant infrastructure management and specialized developer skills, creating barriers for many businesses. Data ingestion, transformation, and optimization can be complex and time-consuming, hindering the rapid deployment of AI solutions.
Solution
Dataworkz provides an all-in-one RAG-as-a-Service platform that enables businesses to rapidly build, deploy, optimize, and scale Generative AI applications without the complexities of infrastructure management. The platform offers a composable AI stack, allowing users to select their preferred vector database, embedding model, and LLM, avoiding vendor lock-in. Dataworkz facilitates no-code data transformation, enabling users to ingest and prepare data from various sources for use with LLMs. The platform also provides end-to-end traceability, allowing users to observe and optimize performance by understanding the underlying processes.
Target Audience
Dataworkz targets businesses looking to enhance customer experiences, improve employee productivity, and democratize data analysis through RAG-powered Generative AI applications.
Features
- Composable AI stack for selecting vector databases, embedding models, and LLMs
- Hybrid search and retrieval using lexical and semantic search with knowledge graphs
- No-code data transformation for ingesting and preparing data from various sources
- End-to-end traceability for observing and optimizing performance
- Support for OpenAI, Vertex AI, and AWS Bedrock LLMs
- SOC2 Type 2 certification, demonstrating commitment to data security and privacy