Context

Chatbot as a Search Engine (Knowledge Centralization)

No matter the size of an organization, its knowledge base is rarely organized well. Documents are kept in randomly placed folders. Staff are unable to tell where to find sources for Important rules, policies, and bylaws. 

The cumulative knowledge and information base of an organization is invaluable – and yet due to pushing organization and maintenance to the side, it becomes almost impossible for staff to search, obtain, and source that information. 

Symphona can help make that information easily accessible to everyone in your organization. 

Solution

How Symphona Helps

Your organization can train Converse generative AI agents on any knowledge base and use for a variety of capabilities, one being as a search engine for their documents and information. Converse is no-code, allowing for people of any technical skill to create these agents. Furthermore, with Symphona operating on a usage-based system, your organization only pays based on how many agent interactions you use.

By training a Converse generative agent on an organization’s digital knowledge base, the agent will learn about all the information it receives, including its location, applicability, and how it relates to other information in the system. By leveraging its LLM technology, the agent can accept and reply to queries submitted by staff about locating or verifying specific internal business information. The agent will speak using organic and formal language. 

When search engine chatbots provide information based on a query, they will additionally share a link to the sources they found. This provides staff with a variety of opportunities, such as being able to locate documents, double-check information, or research further. Chatbot responses will always include sources for any information they retrieve. 

Since information accuracy is critical when searching for internal business information, search engine chatbots are designed with low response variability, which prevents hallucinations from occurring. If a chatbot is unable to find information corresponding to a query, it will simply reply that it was unable to obtain a result based on the information provided. This ensures complete accuracy.

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