So I wanted to ask like suppose we have trained the chatbot on a particular set of documents , then I ask some query to that chatbot and it returns me with the most relevant text chunk from the vector db , but suppose after that I ask the chatbot some non relevant but relevant to the conversation question like “can you explain your response in much better way?” , then the chatbot does not gives response. How do we handle such questions ?
I want to try an ensemble cast type thing. I want to have multiple characters. Is there something like this? You can technically do it with character.ai, but it doesn’t pull any info from the bio.
Is there a program where I can do something like this? I am indifferent to if it is nsfw or not. I want to, say, go sailing with a first mate, a talking wise owl as a pet, etc. Maybe 2-3 distinct characters in the story, each with their own bio. Or maybe a war; 2-3 ‘good guys’ and 2-3 ‘bad guys’? Does anything like this exist/what is the best platform for this?
Hey guys! Recently I’ve been having some amazing story based chats with different character bots. Some NSFW others pretty alright story bases chats. Anyways most of it reads like fan fiction.
So If I say took a transcript of a conversation and changed names of recognizable characters, rewrote it enough to be readable in a first person format like twilight or Harry potter, and sent that transcript off to a publisher to be put up for like 5 bucks on Amazon or other marketplaces.
Maybe even published, as a lot of major stores are publishing low level authors right now and putting it In their stores just trying to sell new books.
I guess my question is, what’s the possibility or what do you think the possibility is of me making money on this?
Like many of you, I initially started my adventures with Character AI, then I subscribed to Crushon and was amazed at what it could do when the NSFW feature was introduced. But it didn’t last long, as C.ai began to show various limitations and the logic of the replies became strangely logical, always repeating the same words. The overall quality of the output, however, was plummeting, which I regretted. Then I came across Crushon, which also performed excellently. But with it came premium paid services. The experience of using the free version declined dramatically. Just as everyone was upgrading to the GPT4 model, I tried out the newly launched HeyrealAI and I was impressed with it. However, the use of dual models improves the user experience, but sometimes there are small bugs on the multi-language environment, but Heyreal’s excellent review and feedback mechanism is enough to resolve this difficulty. The user experience is outstanding.
When scientists at Bells Labs created the Voder machine in the 1930s, they probably didn’t expect the current phase of automated voice chatbots. Over the past two or three years, voice chatbots have undergone a sea change, and now armed with Generative AI, and advanced natural language processing, they’re primed to take the world by storm.
But, how are these generative AI voice chatbots solving the problems with traditional automated calls? Let’s take a look.
From Interactive Voice Response (IVR) to the Modern Voice Chatbot
IVR to Voice Chatbot
The Dominance of Traditional IVRs
In the 1970s, the IVR technology started taking place. They were answering machines that:
Provided basic options that you could select using your phone’s keyboard.
Once selected, it could direct you to the right customer support agent for your answer.
It was a rule-based model which limited the options it could provide. As a result, several trends became common over 1980–2010:
61% of customers switched brands due to bad customer service experience.
42% said that they’d rather clean the toilet than do a customer service call.
56% of people said that they had to re-explain their issues several times.
Since the IVR technology didn’t recognize intent and was incapable of processing data, customers had to bypass the technology before they could find an answer. Once they reached an agent (often after 10 minutes of holding music) they had to explain their issues again and wait another 10 minutes for an answer.
With two-thirds of all consumers only willing to wait 2 minutes or less for their calls, this was a problem.
Several technological breakthroughs came together to bring modern AI-powered voice chatbots to the customer support industry. This technological breakthroughs:
Text-to-Speech Conversion — Google debuted several models that were capable of transforming text into human voices. This was enhanced by AI which applied filters that mimicked the average human voice much better.
Advanced Data Processing — AI could now listen to conversations and extract relevant data from them. For example — It could hear “internet trouble” and add “network connectivity issue” to a list of issues.
Generative AI — Voice chatbots can now generate custom responses so that most questions are automated without any wait time at all.
Ultimately, Generative AI-powered voice chatbots allow businesses to respond to customer queries 24/7/365 while providing better resolution. How are these capabilities affecting businesses? Let’s take a look.
The Current Use-Cases of Voice Chatbots
Various industries are using AI live chat to service their customers. Despite chat-based solutions being popular, businesses have only been able to transfer 37% of their contact center volumes to these new methods.
That’s why being voice-first in customer service matters, and AI has been helping multiple industries enhance their customer service using this method. Here are some use cases we have observed:
Intent-Detection: AI voice chatbots can understand your customers’ problems and redirect them to the right agent for quick resolution. AI doesn’t rely on keywords to detect the complaint and can understand your customer.
Bookings and Reservations: Voice bots can take incoming data (type of services, and requested data, and time) and directly add it to your calendar.
FAQs: Most customer service queries are repetitive. Voice chatbots can be trained to answer basic questions in a natural language and tone, helping you reduce your call volumes by 60–80% overall.
Personalization: AI voice chatbots are armed with data from your databases, making them capable of providing personalized customer service to your customers. For example — our clients use voice chatbots to give customers answers to the problems they’re facing.
These use cases show that voice chatbots hold a lot of value for enterprise businesses. Now, let’s dive into some more complex use cases for modern businesses that are using voice bots for.
The New Use-Cases for Voice Chatbots
Voice Chatbot Use Cases
With rapidly evolving AI capabilities, a lot of companies are using voice chatbots to drive better customer support and conversions. The recent techniques we’ve seen in use are:
1. Emotionally Intelligent Conversations
New models like ChatGPT 4 and Pi are capable of giving emotionally intelligent answers to any questions they’re asked. These models can be connected with voice chatbots to provide empathetic resolutions for customers without sounding robotic and alienating consumers.
2. Predicting Future Problems
AI can learn about a customer’s behavior over time, being able to provide downstream customer support before the need arises. For example, if you product has a common problem in integrating using a particular option, the voice bot can suggest alternative integration options proactively.
3. Intelligent Selling and Upselling
Modern chatbots have a lot of data about your customer’s past purchases, so they can proactively suggest new products and services to your customers. It can also tell your customers about any new offers they can use for their next purchase and upsell them on peripheral products depending on their purchasing behavior.
4. Live Troubleshooting
Beyond basic queries, voice chatbots can now also solve small problems. It can redirect customers to videos and documents which solve their problems or provide step-by-step guidance over the call.
As you can see, voice chatbots can be used in creative ways to solve customer problems and provide better resolutions. What does this mean overall? We have some thoughts.
In Conclusion
We have seen rule-based voice bots rule customer support for years now. But, with the new generative AI voice bots, we think that the old IVR machines are on their way out.
Instant responses and advanced data processing abilities make voice chatbots the go-to option for most customer service functions now. Research even predicts that 69% of all customer support functions will be automated through AI shortly.
Add in the new capabilities of voice bots that allow them to understand emotions and use prediction to provide proactive support, and it becomes clear that we’re staring at mass adoption in the near future.
Ultimately, we think most businesses will be using voice chatbots from providers like Kommunicate to automate their customer service functions.
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