With every personalized AI-presented presentation and website generated, Cast also generates a personalized next-generation chatbot. This generative and personalized chatbot, "Ask Me Anything," learns from both your entire tech stack and all your products.
Cast generates a personalized next-generation chatbot embedded in every personalized AI-presented presentation.
Imagine presenting a 25-slide presentation for a board meeting you meticulously created — only to be interrupted with questions on the 5th slide.
The same is true when a customer-facing team member presents a business review to a customer and is interrupted with questions — assuming you are lucky enough to have all the customer executives show up for the meeting.
Generally, audience members interrupt for four reasons — via Presentation Training Institute — namely,
"Clarification and Understanding" and "Interest and Excitement" interruptions during presentations from audiences are welcome and a good sign.
Board members or customers ask questions is because you have provided helpful context that engages them and prompts them to ask questions.
Since Cast Personalized Presentations are AI-generative and AI-presented — to engage and influence all users and executives — we built AI Agents directly into the presentations to empower them to dig deeper with questions. AI Agents are ready to answer account-specific, real-time questions, ensuring decision-makers get the insights they need when they need them.
Note, Interruptions because of Dominance and Control and Timing and Pacing Concerns do not apply to AI-presented presentations.
Before the introduction of LLM-based chatbots, e.g., ChatGPT, chatbots had an awful reputation akin to IVR from the 2000s. They rarely answered questions correctly and frustrated users versus helping them.
Over the years, AI agents have evolved significantly, growing in capabilities and the value they bring to businesses. Let us look at the evolution of AI agents.
Early versions of AI agents, like the initial iterations of Drift (used for lead generation) and Intercom (used for customer support), were based on simple, rules-driven logic. These chatbots followed pre-programmed rules with no ability to learn or adapt, providing essential, scripted responses limited to predefined interactions.
These agents pulled data from static sources like knowledge bases and help-desk articles. While they could access more information than their predecessors, their insights were often limited by the frequency of content updates, making it challenging to offer the most current information.
AI agents evolved to access specific systems like Zendesk, allowing them to analyze real-time data such as "case descriptions" and "case resolutions." This marked a significant improvement, enabling more dynamic responses. However, these agents were still confined to the data available within these systems, limiting their broader contextual understanding.
As the name suggests, these AI Agents can run diagnostics as the user communicates with the AI agent. Think AT&T AI Agent running a check on your high-speed fiber to see if there is a reported outage or check for one in real-time by running a diagnostic tool.
These agents could now integrate data from dynamic databases and static knowledge bases, providing more accurate and context-aware responses. However, their ability to deeply understand and synthesize information across varied sources was still in development, leading to responses that sometimes lacked the nuance needed for complex queries.
Agents like Salesforce Einstein introduced the ability to access multi-tenant databases and leverage predictive analytics, offering broader insights. However, these insights were often standardized, and the agents were limited in contextualizing data across different, sometimes siloed, sources.
Agentic AI Agents Introduce autonomy and learning capabilities, allowing agents to adapt to new information and operate with contextual awareness. These agents are capable of making decisions independently and adjusting their actions based on real-world feedback without requiring human intervention at every step.
RAG and RAG-less AI agents: Some of these agents use Retrieval-Augmented Generation (RAG) to fetch real-time data, while others (RAG-less agents) function without retrieving information from external sources, relying on internalized knowledge for decision-making and adaptation.
Extensible RAG-less Agentic AI Agents are advanced AI systems that not only possess autonomy and learning capabilities but can also extend their functionality by interacting with external APIs. These agents can dynamically call APIs by passing in specific parameters, retrieving real-time results, and converting the outputs into conversational or actionable responses.
This ability allows them to access and integrate data from various sources—such as weather updates, financial systems, order status, or customer databases—enabling them to provide highly relevant and context-aware responses. By combining autonomy with API extensibility, these agents can perform more complex tasks, such as processing transactions, offering personalized recommendations, or executing business operations without human intervention. This makes them highly adaptable across different use cases, further bridging the gap between AI systems and real-world applications.
A major problem with RAG systems that access more that 2-3 data sources is latency. Cast connects with several data sources simultaneously, writing cross-product queries. RAG system fall short as an infrastructure, but that does not stop Cast from learning from RAG systems.
Every time Cast generates a personalized AI-driven presentation or website, it also creates a next-generation personalized chatbot called “Ask Me Anything.” This chatbot is generative and adaptive, drawing insights from your entire tech stack and all your products. Cast embeds these advanced AI chatbots within each personalized presentation, ensuring a seamless and interactive user experience.
They not only learn from these sources, but they also provide context — similar to when you present to the board or your customer — before being interrupted with intelligent questions.
This updated approach positions your solution as the most advanced and beneficial AI solution for your customers, offering unique capabilities that set it apart from previous generations.
