Abstract
Existing methods for generating business prospects can have limitations related to latency, data inaccuracy, and information sparsity, which may result in outdated or irrelevant leads. A described autonomous, multi-agent system can utilize generative artificial intelligence models to help address these potential issues. The system can be configured with a hierarchical architecture where, for example, a master orchestrator agent can delegate specific tasks to specialized subagents for the discovery, verification, and firmographic enrichment of business entities. These agents can perform functions such as semantic web scanning, cognitive auditing of websites, and multi-source data validation. This approach may improve the timeliness, accuracy, and scalability of acquiring market intelligence from unstructured web data, providing more comprehensive prospect information for integration into various downstream business applications, such as customer relationship management platforms or sales automation tools.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Narravula, Sundeep; Shettigar, Manya; Pipia, Demetre; Tan, Robert; and Aliminati, Janga, "Autonomous Multi-Agent System for Business Entity Discovery, Verification, and Enrichment", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/11272