Agentset: Build AI Chat and Search

Agentset delivers high accuracy on your data before any customizations. It achieves industry-setting benchmarks for MultiHopQA and FinanceBench, ensuring reliable answers. This is achieved through optimized document processing and retrieval algorithms, reducing the need for extensive manual data preparation and fine-tuning, which often plagues other RAG implementations.

Agentset natively processes images, graphs, and tables alongside text. This allows you to answer questions from all parts of your knowledge base, enhancing the richness and comprehensiveness of your AI applications. This capability is built-in, unlike many RAG systems that require custom integrations for non-textual data, saving developers significant time and effort.

Agentset automatically cites the sources of answers, allowing users to verify the information's origin. This builds trust and transparency, a critical feature for applications dealing with sensitive or critical information. This citation feature is integrated directly into the response, making it easy for users to understand the source of the information.

Agentset supports metadata filtering, enabling answers based on a subset of your data. This allows for more precise and relevant responses, especially in large datasets. This feature allows for more granular control over the data used to generate answers, resulting in more accurate and contextually relevant results.

Agentset provides a customizable chat interface for quickly capturing external feedback. This allows for easy integration into existing applications and facilitates rapid iteration based on user input. The interface is designed to be easily integrated into existing applications, allowing for quick feedback and iterative development.