Plain Vanilla Chatbots: 99% of Organizations Still Rely on These
These standard chat workflows follow a sequential, activity-based structure.
Typically, the user selects from a predefined list of limited questions. Each question triggers a structured flow. For example, checking an Amazon order:
Select the order.
System presents next predefined options like:
Status of my order
Refund status
Schedule return shipment
How to set up this product
This model is static, rigid, and predictable. While effective for simple queries, it lacks flexibility — which is why organizations are transitioning to more dynamic AI-based chatbots.
Read More: Does AI Astrology Works
How AI Chatbots Are Changing the Game
The real power behind modern AI chatbots is the Large Language Model (LLM).
LLMs have existed for decades, but only recently — thanks to massive GPU compute and advancements from companies like Google, Meta, Microsoft, and OpenAI — have they achieved human-like conversation quality.
LLMs serve as a platform and architecture that can be fine-tuned for specific needs.
As discussed in the “Does AI Astrology Work?” blog, out-of-the-box AI is great for generic tasks but fails at domain-specific accuracy. This is where Vani AI stands apart — built with:
deeper guardrails
higher accuracy
specialized agents
cleaner data
domain-specific intelligence
Generative AI vs. Agentic AI
Generative AI creates content such as text, summaries, or images. You give the model a prompt, and it outputs content.
Agentic AI, on the other hand, handles complex multi-step tasks, reasoning, and decision-making with a high level of autonomy.
Key Features of Generative AI
1. Content Creation
Generative AI excels in generating essays, lists, explanations, and more. Apps like ChatGPT create human-like responses based on user prompts. With Vani AI, content creation is currently not the core requirement.
2. Data Analysis
Gen AI can analyze large datasets, detect patterns, and simplify complex workflows — including interpreting kundali charts.
3. Adaptability
It can adjust outputs based on user feedback, improving relevance and personalization.
4. Personalization
Gen AI can recommend custom experiences. Retail brands use this to personalize customer journeys based on preferences.
Key Features of Agentic AI
1. Decision-Making
Handled by the Orchestrator Agent, which decides which specialized agent should handle a specific query.
2. Problem-Solving
Agentic AI follows a four-step loop: Perceive → Reason → Act → Learn
Vani AI gathers data, analyzes it, interprets the situation, then takes action.
3. Interactivity
Agentic AI proactively interacts with real-time data sources. In Vani AI’s case, it reads dynamically generated charts and values.
4. Planning
Vani AI can handle complex astrological questions. The Orchestrator + Memory Agent together:
plan responses
review agent outputs
challenge inaccuracies before answering
maintain context over long conversations
Conclusion
If you’ve read this far, you now understand:
Vani AI is primarily an Agentic AI system that incorporates selective strengths of Generative AI.
When you talk to Vani:
it remembers you,
adapts to you,
and becomes more personalized with every interaction.