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LLM/Generative Augmentation

Engage Customers Through Conversations

Build trust with Factual, Reliable Al Responses

Augment LLMs with drill-through to underlying datasources and documents

Use Cases

Use Case 1: Engage Customers through Conversation

For: Al Chatbot Developers, CX Architects

Forgetful bots that can't keep up with customers' conversations leaving your customers frustrated? Agolo's entity graph ensures seamless, extended dialogues that keep the conversation going.

  • Conversational Al often suffers from amnesia, with context losses leading to repeated or irrelevant responses. With Agolo, a chatbot can sustain longer, contextually-aware conversations without "forgetting" past interactions.

  • Using Agolo's Entity Graph as Conversation Entity Memory, a LLM can now tap into contextually relevant data throughout the dialogue. The result? An Al that can manage tokens effectively, allowing for conversations that are consistent, logical, and devoid of repetitive loops, greatly enhancing customer experience.

Use Case 2: Build Trust with Factual, Reliable A.I. Responses

For: A.l. Application Developers

Al misinformation can erode user trust - ensure that every Al response is rooted in a trusted source with Agolo's entity graph.

  • Generative Al can provide answers that aren't entirely accurate. But with the world demanding factual, transparent, and deterministic Al responses, there's no room for misinformation.

  • By integrating Agolo's Entity Graph with LLMs and using lower model temperatures, Al systems can ensure that the information being served is from a reliable source of truth. Every Al-generated response is not just accurate but also grounded in trusted data controlled and moderated by you, eliminating hallucinations and cementing user trust.

Use Case 3: Augment LLMs with Drill-Through to Underlying Data Sources and Documents

For: AI Application Developers

Citing sources for information is pivotal in research. Agolo's entity graph enables your analysts and Al assistants to provide not just answers, but also their trusted sources.

  • Today's research landscape demands transparency and traceability. While Generative Al-based applications can provide quick answers, knowing where that information comes from is equally crucial.

  • Agolo allows LLMs to list the sources of their information, be it a document, article, or URL. So, when a researcher inquires about a topic, the Al not only delivers a comprehensive answer but also cites the exact source. This transparency ensures that every piece of data is backed by a trusted source, enhancing the credibility of Al-backed research.