Data.world's AI Context Engine Enables AI-ready Data with Accuracy and Explainability
Solution allows an enterprise to use generative AI for secure conversations with its own data.
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Data.world, an enterprise data catalog platform, has rolled out AI Context Engine. Enterprises can now use generative AI to have secure conversations with their own data.
All teams want to realize the ROI that AI delivers, but few have unlocked a way to actually do so. Teams use tools like ChatGPT to chat with the internet’s data, but they have yet to chat so effectively with their own data. With the AI Context Engine, a team member can query their data with questions like: "How does our organization define current retention rate, and how have these rates changed over the last five years?”
Incorporating business context and data into AI responses is now easier than ever. The AI Context Engine is built on data.world’s knowledge graph architecture, which means that AI-generated answers are more accurate than LLMs relying solely on SQL databases. Teams can finally trust the answers and insights from their own structured data.
Chatting with proprietary data isn’t a brand new functionality, but to date, chatting with proprietary data to this degree of explainability has not been available. Where once there was a black box, there is now a detailed explanation of an LLM’s path to response. Explainability is useful for enterprises that want to build a treasure trove of widely-used insights from their data.
The AI Context Engine also allows for higher levels of LLM response accuracy than have previously been seen on the market. Many businesses struggle to integrate AI with their own, unique data, leaving critical insights untapped. Current market solutions have been hit or miss when it comes to accuracy.
Similar tools lack either the capability to build custom AI applications or require teams to arduously train AI models and build disparate, non-reusable, one-off applications. The AI Context Engine creates models that are flexible, repeatable, and scalable.
More specifically, here’s what teams are doing with The AI Context Engine:
- Building AI-powered chat: Using the engine to obtain quick answers to complex business questions through a natural language interface that seamlessly scales with data volume
- Obtaining explainable responses: Responses are based on a comprehensive data catalog and knowledge graph, ensuring they are fully traceable, auditable, and reusable
- Automating agents: Automate agents to assist with glossary lookups, query validation, and explanations, delivering analytics and decision support, all tailored to an organization’s specific business knowledge
- Mapping transparent ownership: Build a map of data ownership and stewardship across the organization
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