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The three Vs of today’s chatbots: Voice, Vernacular and Video

3 minutes, 28 seconds read

More than 70% of consumers in Australia, the UK and France and over 50% in the US and Germany report interacting with chatbots at least once during the last year. A recent study by Forrester states that 57% of the organizations globally are already using chatbots indicating the organizations’ affinity towards helpdesk and customer support automation.

In today’s time, where meeting people face-to-face to close deals is dubious; chatbots with voice, vernacular (multilingual), and video emerge as a savior. Especially for SMEs, where persuasion plays a key role in signing a contract, chatbots with video conferencing features and local language support can make conversations more seamless.

Let’s delve deeper into the voice, vernacular and video conferencing features of chatbots and their use cases.

Voice-enabled chatbots

Voice-enabled chatbots or simply voice chatbots can interact with users via text or voice commands. Based on the input command type (voice/text), these bots reply to the user accordingly. 

In India, nearly 30% of Google searches made in 2019 were voice-based. Moreover, Google Assistant recognizes Hindi as the second-most utilized language for voice globally. Chatbots enabled with voice add accessibility to a wider range of customer base. Voice-based conversational chatbots add speed to processing the command as it need not wait for the user to type the query. 

Businesses like beauty & spa, healthcare, travel, FMCG, Restaurants, and many more can use voice-driven chatbots to answer customer queries and automate their helpdesk tasks.

Vernacular language support or multilingual chatbots

A study by KPMG and Google reveals that the native Indian language user base will reach 536 million by 2021. The study conducted in 2017 highlights some of the most critical internet challenges faced by the Indian diverse populace:

  • 70% of Indians face challenges in using English keyboards.
  • 60% of Indians find limited language support to be the barrier to adopting digital technologies.
  • 88% of users are more likely to respond to a digital advertisement in their local language as compared to English.
  • Nearly 25% of the Indian language internet users face challenges concerning the use of e-commerce payment interfaces, leading to dropouts at the time of final checkouts.

The above data indicates the need for multilingual support in any customer-facing application. In fact, by next year, nearly 75% of internet users in India would be a vernacular content user base. Brands like Godrej have already started leveraging regional language on its website. Multilingual chatbots can personalize conversations and make the technology more adaptable to the native users. 

Indian chatbots like Hitee (designed for Indian SMEs) support several Indian regional languages including Hindi, Tamil, Bengali, Telugu, Gujarati, Kannada and Malayalam.

AI Chatbot in Insurance Report

AI in Insurance will value at $36B by 2026. Chatbots will occupy 40% of overall deployment, predominantly within customer service roles.
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Video conferencing chatbots

In the backdrop of COVID-19 pandemic, travel restrictions will pertain long. Therefore, most of the personal interactions will be made through video conferencing software. Video bots vs video conferencing software: For a growing business, scheduling/setting up meetings for every customer can be tedious. Especially when the meetings are regarding product demo, sampling, FAQs, it completely makes sense to opt for automation. This way, business owners can release their time for critical business decisions.

For example, manufacturing businesses/wholesalers can record the product demo, include them into the chatbot workflow and relieve themselves of the routine demonstrations.

Usually, private clinics maintain a register/excel for noting down the appointments of the day. Then, they switch to a platform that supports video chats (WhatsApp, Skype, Google Duo, Zoom) to consult patients. Missing an appointment/patient record, communication gap, etc. are very common in this scenario. 

Thus, private healthcare practitioners can use chatbots to schedule appointments automatically and converse with patients through the same chatbot interface. 

Similarly, stockbrokers, wealth managers, legal consultants, finance service providers, tour operators, and tax consultants can use video conferencing chatbots for different levels of engagement with their clients. 

Read more: Conversational Chatbots for SMEs to continue business from home

Enterprise chatbots can integrate with the organization’s workflows to make them capable of routing customer queries to relevant teams/agents whenever the need arises. Bots with video conferencing features can extend support to Video KYCs by automating document collection and verification processes using in-built facial recognition mechanisms. This can help businesses (BFSI, NBFC) speed-up their customer onboarding process.

Need a chatbot for your business? Check out Hitee — a Make-in-India conversational chatbot that coverts 5X more leads!

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Machines That Make Up Facts? Stopping AI Hallucinations with Reliable Systems

There was a time when people truly believed that humans only used 10% of their brains, so much so that it fueled Hollywood Movies and self-help personas promising untapped genius. The truth? Neuroscientists have long debunked this myth, proving that nearly all parts of our brain are active, even when we’re at rest. Now, imagine AI doing the same, providing information that is untrue, except unlike us, it doesn’t have a moment of self-doubt. That’s the bizarre and sometimes dangerous world of AI hallucinations.

AI hallucinations aren’t just funny errors; they’re a real and growing issue in AI-generated misinformation. So why do they happen, and how do we build reliable AI systems that don’t confidently mislead us? Let’s dive in.

Why Do AI Hallucinations Happen?

AI hallucinations happen when models generate errors due to incomplete, biased, or conflicting data. Other reasons include:

  • Human oversight: AI mirrors human biases and errors in training data, leading to AI’s false information
  • Lack of reasoning: Unlike humans, AI doesn’t “think” critically—it generates predictions based on patterns.

But beyond these, what if AI is too creative for its own good?

‘Creativity Gone Rogue’: When AI’s Imagination Runs Wild

AI doesn’t dream, but sometimes it gets ‘too creative’—spinning plausible-sounding stories that are basically AI-generated fake data with zero factual basis. Take the case of Meta’s Galactica, an AI model designed to generate scientific papers. It confidently fabricated entire studies with fake references, leading Meta to shut it down in three days.

This raises the question: Should AI be designed to be ‘less creative’ when AI trustworthiness matters?

The Overconfidence Problem

Ever heard the phrase, “Be confident, but not overconfident”? AI definitely hasn’t.

AI hallucinations happen because AI lacks self-doubt. When it doesn’t know something, it doesn’t hesitate—it just generates the most statistically probable answer. In one bizarre case, ChatGPT falsely accused a law professor of sexual harassment and even cited fake legal documents as proof.

Take the now-infamous case of Google’s Bard, which confidently claimed that the James Webb Space Telescope took the first-ever image of an exoplanet, a factually incorrect statement that went viral before Google had to step in and correct it.

There are more such multiple instances where AI hallucinations have led to Human hallucinations. Here are a few instances we faced.

When we tried the prompt of “Padmavaat according to the description of Malik Muhammad Jayasi-the writer ”

When we tried the prompt of “monkey to man evolution”

Now, if this is making you question your AI’s ability to get things right, then you should probably start looking have a checklist to check if your AI is reliable.

Before diving into solutions. Question your AI. If it can do these, maybe these will solve a bit of issues:

  • Can AI recognize its own mistakes?
  • What would “self-awareness” look like in AI without consciousness?
  • Are there techniques to make AI second-guess itself?
  • Can AI “consult an expert” before answering?

That might be just a checklist, but here are the strategies that make AI more reliable:

Strategies for Building Reliable AI

1. Neurosymbolic AI

It is a hybrid approach combining symbolic reasoning (logical rules) with deep learning to improve factual accuracy. IBM is pioneering this approach to build trustworthy AI systems that reason more like humans. For example, RAAPID’s solutions utilize this approach to transform clinical data into compliant, profitable risk adjustment, improving contextual understanding and reducing misdiagnoses.

2. Human-in-the-Loop Verification

Instead of random checks, AI can be trained to request human validation in critical areas. Companies like OpenAI and Google DeepMind are implementing real-time feedback loops where AI flags uncertain responses for review. A notable AI hallucination prevention use case is in medical AI, where human radiologists verify AI-detected anomalies in scans, improving diagnostic accuracy.

3. Truth Scoring Mechanism

IBM’s FactSheets AI assigns credibility scores to AI-generated content, ensuring more fact-based responses. This approach is already being used in financial risk assessment models, where AI outputs are ranked by reliability before human analysts review them.

4. AI ‘Memory’ for Context Awareness

Retrieval-Augmented Generation (RAG) allows AI to access verified sources before responding. This method is already being used by platforms like Bing AI, which cites sources instead of generating standalone answers. In legal tech, RAG-based models ensure AI-generated contracts reference actual legal precedents, reducing AI accuracy problems.

5. Red Teaming & Adversarial Testing

Companies like OpenAI and Google regularly use “red teaming”—pitting AI against expert testers who try to break its logic and expose weaknesses. This helps fine-tune AI models before public release. A practical AI reliability example is cybersecurity AI, where red teams simulate hacking attempts to uncover vulnerabilities before systems go live 

The Future: AI That Knows When to Say, “I Don’t Know”

One of the most important steps toward reliable AI is training models to recognize uncertainty. Instead of making up answers, AI should be able to respond with “I’m unsure” or direct users to validated sources. Google DeepMind’s Socratic AI model is experimenting with ways to embed self-doubt into AI.

Conclusion:

AI hallucinations aren’t just quirky mistakes—they’re a major roadblock in creating trustworthy AI systems. By blending techniques like neurosymbolic AI, human-in-the-loop verification, and retrieval-augmented generation, we can push AI toward greater accuracy and reliability.

But here’s the big question: Should AI always strive to be 100% factual, or does some level of ‘creative hallucination’ have its place? After all, some of the best innovations come from thinking outside the box—even if that box is built from AI-generated data and machine learning algorithms.

At Mantra Labs, we specialize in data-driven AI solutions designed to minimize hallucinations and maximize trust. Whether you’re developing AI-powered products or enhancing decision-making with machine learning, our expertise ensures your models provide accurate information, making life easier for humans

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