Enable Better Generative AI

DataWalk’s graph AI platform can augment your LLM with your organization’s structured and unstructured data, so the LLM can reliably answer even internal organization-specific questions. You can effectively ground (using RAG, or Retrieval Augmented Generation) either a commercial LLM such as GPT-4 via API, or an open source LLM such as Llama.

DataWalk incorporates your organization’s knowledge with valuable context and relationship information so that the LLM summary is grounded in facts. The responses are more relevant, accurate and trusted and can include links to the documents upon which the summary response was generated. DataWalk can act as an agent, breaking down a question into appropriately sized tasks and sequencing them to the LLM via the DataWalk API.

With DataWalk you can simplify LLM deployment as there’s no need to write code to coordinate between your company’s information and your chosen LLM.
Unlike many other grounding options which require you to write the agent code coordinating the calls and tasks between different system components, DataWalk has this capability built-in for many LLMs including ChatGPT and Llama.

DataWalk’s graph AI platform unifies all the unstructured and structured data from across your organization’s many disparate systems and makes it available in a clean, normalized knowledge graph. The DataWalk knowledge graph provides a unique combination of remarkable flexibility to connect new data, exceptional performance for graph algorithms, and effective handling of vast amounts of data, all without coding.