Latest news with #conversationalAI
Forbes
3 days ago
- Business
- Forbes
Voice-To-Voice Models And Beyond Meat: Still Not Ready For Mass Consumption
Arkadiy Telegin is the cofounder and CTO of Leaping AI, a conversational AI platform supporting customer experience departments worldwide. I'm vegan. So when plant-based meat started going mainstream, I was elated. The tech was impressive, the marketing confident and, for a moment, it felt like we were on the cusp of a food revolution. Impossible Burgers hit Burger King. Beyond was everywhere. Investors poured in. The future, it seemed, had arrived. Except it hadn't. Today, plant-based meat is still a niche. Prices are high, availability is inconsistent and adoption is slower than expected. It's not that the products disappeared. They just haven't yet integrated into everyday life the way we imagined. This is a classic case of psychological distance: a cognitive bias where things that feel close because they're exciting or well-promoted turn out to be farther off than we think. In voice AI, voice-to-voice model development is going through the same thing. Despite recent latency, reasoning and sound quality improvements, there's been a stubborn insistence on using older, more established technologies to build conversational AI platforms. Why is that? After LLMs appeared, the first commercial voice AI applications all used a 'cascading' approach following a three-step sequence: • Speech-To-Text (STT): Transcribe the user's speech to text. • Large Language Model (LLM): Use an LLM to respond to the transcribed user's speech. • Text-To-Speech (TTS): Synthesize speech from your response and play it back. This is a standard, time-tested approach that's been in use even before LLMs came around, primarily for language translation. Then, last fall, OpenAI launched its Realtime API, which promised a one-step speech-to-speech AI model capable of parsing audio directly to generate real-time responses, resulting in agents that sound much more human, can natively detect emotions and can be more 'tone aware.' OpenAI's entry into the space was the most commercially significant development yet, leading many to anticipate a new era for single-step voice-to-voice AI models that could feasibly be used in real-world applications. Over six months later, while Realtime API's launch has created a lot of excitement around direct speech-to-speech AI models—the recently announced Nova Sonic model from Amazon and Sesame's base model for its Maya assistant are just a few examples—when it comes to production-level applications, my industry colleagues and customers alike are still more comfortable using the status quo of multi-step pipelines, with no plans to change that any time soon. There are a few key reasons why that is the case. Working with audio presents inherent difficulties. Text is clean, modular and easily manipulated. It allows for storage, searchability and mid-call edits. Audio, in contrast, is less forgiving. Even post-call tasks like analysis and summarization often necessitate transcription. In-call operations, such as managing state or editing messages, are more cumbersome with audio. Function calling is crucial in production use-cases—fetching data, triggering workflows, querying APIs. Currently, one-step voice-to-voice models lag in this area. Stanford computer science professor and founder Andrew Ng, who also cofounded the Google Brain project, has publicly shared some of these limitations. It is much easier to create and curate a good function-calling dataset for a text-based model than for a multimodal model. As a result of this, the function-calling capabilities of text-first models will always outperform those of voice-to-voice models. Considering that function calling is not perfect even for text models yet and is a crucial requirement for commercial applications, it will take some time until voice-to-voice catches up to meet production standards. Ng shares the example of gut-checking responses like "Yes, I can issue you a refund" to ensure refunds are allowable against the current company policy and how an API can be called to issue that refund if the customer requests one. That's more doable to build in a cascading workflow but not as reliable for one-step pipelines for the reasons stated above. Since OpenAI launched its Realtime API, there have been a number of complaints that have made developers uneasy about using it in production, including audio cutting off unexpectedly and hallucinations interrupting live conversations. Others have complained of hallucinations that don't get captured in the transcript, making it challenging to catch and debug them. This isn't to say one-step voice-to-voice AI is a dead end. Far from it. The potential for enhanced user experience—handling interruptions, conveying emotion, capturing tone—is immense. Many in the industry, our team included, are actively experimenting, preparing for the moment when it matures. Startups and major players alike continue to invest in speech-native approaches as they anticipate a more emotionally resonant, real-time future. In other words: It's a matter of when, not if. In the meantime, multi-step pipelines for voice-to-voice AI models continue to win on reliability and production-readiness. With steady improvements, particularly in behavior and function calling, the moment for single-step models will come. Until then, the trusted cascading approach will carry the load, and I'm still not eating at Burger King. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
The Independent
3 days ago
- Business
- The Independent
Worth talking about: the future of conversational AI in business
Cognigy is a Business Reporter client Stunning advances in AI technology over the past couple of years are creating new ways for organisations to conduct more meaningful and natural conversations with their customers. One such technology is conversational agentic AI. Now launching into the UK, Cognigy is a specialist in enterprise conversational AI. Its flagship solution, combines generative and conversational AI to deliver hyper-personalized, multilingual service across channels, empowering enterprises with scalable Voice and Chat AI Agents, Agent Copilot tools, and real-time support. With proven success in industries like banking, travel, and utilities, and trusted by major brands including Nestlé, Lufthansa, and Mercedes-Benz, Cognigy is setting a new benchmark in intelligent automation for contact centres worldwide. We sat down with Sebastian Glock, Technology Evangelist at Cognigy, to ask him how changes in conversational agentic AI are unfolding, where it's all headed and what organisations need to keep in mind as they explore the new possibilities. How capable is conversational AI today and what could it offer in the future? The chatbots and voice bots of a few years ago often disappointed users with bad answers or by the need to respond in unnatural ways, such as giving single-word requests like 'refund' instead of using full sentences. They were also slow. Above all, they fell short of the hype and expectations that had been built up. People expected science fiction but instead they got 'Sorry, I didn't get that' over and over. The rise of large language models has transformed conversational AI. Yet that's not the full story. LLMs are impressive but can also be unpredictable. Where things really click is when you combine that raw power with structure, purpose and guardrails that contain tight controls, so responses stay relevant, safe and on-brand. AI agents can then conduct interactions with humans in a way that feels natural. Human-to-machine communication becomes almost indistinguishable from human-to-human communication. The ability for a machine to have a smart and contextual conversation with a human is something that was impossible even as recently as two years ago. And even though it's not yet widely implemented, the technology to have effortless, natural and productive interactions is here and it works. Looking ahead, there will soon come a time when humans will prefer to talk to the AI rather than a human contact centre agent. Interactions will become so good that nobody will want to spend the time or effort trawling through a website to find the information they want. Imagine being able to simply talk to a website and it instantly responds with exactly the information you wanted. How can AI customer service agents meet the varying needs of different organisations? AI agents allow companies to combine all the benefits of automation with a greatly improved customer experience that offers less waiting time, better answers and more empathetic communication. At the same time, organisations can decrease service costs by automating their customer conversations. The technology is flexible, allowing organisations to blend human and AI interactions to suit their needs. Intelligent conversation design ensures that if a customer makes a difficult request – for example, asking for a discount that AI cannot authorise – a human will take over. Workflows can be tailored so the AI might say, 'Let me check with my supervisor,' and then follow up with a human-style email for a personal touch, even if the response is AI-generated. For premium brands, an AI can verify identity and route calls, yet every interaction ultimately connects the customer with a human expert. Conversely, some companies may limit human involvement; here, if the AI is unsure, it will call you back with an answer after consulting a human. It's also critical to enable AI agents to access existing data and tools like a CRM or ERP system. This lets the AI understand a caller's recent orders, preferences or past issues. That context allows for a much more personalised and efficient exchange, which makes things smoother for both sides. We also work with our clients to make sure the AI assistant speaks in a way that reflects their brand identity, whether that's professional and formal or more casual and conversational. It's not just about words. It's about pacing, empathy and how solutions are delivered. Done right, it can feel like a natural extension of the brand. What is a good example of how a company is using your AI solutions to benefit their business? The best results come from industries with a high volume of incoming customer enquiries that are typically repetitive. A good example is insurance or finance, where most interactions often involve similar requests. In one case, we work with a large European bank to improve how it confirms appointments for credit requests that customers make online. Previously, skilled loan advisors manually dialled applicants' numbers, but 80 per cent of these calls failed due to no answer, hang-ups or confusion. Now, Cognigy's voice AI agent automatically calls each number, verifies the loan application and asks if the customer is ready to speak with an advisor to complete the process. The AI agent even offers flexible scheduling or records if a customer is no longer interested, with all data fed into the system. About 80 per cent of calls still don't result in a successful loan application, which is the same as before. However, the huge difference here is that it dramatically decreases the workload on loan advisors, saving them countless hours and thousands of unnecessary calls. Most importantly, of the 20 per cent which succeed, about 85 per cent are directly transferred to a human loan advisor which accelerates the process, boosts conversion rates and ultimately generates more revenue for the bank at a much lower cost. How do you answer an organisation's concerns about an AI agent's reliability and compliance with data protection and privacy rules? We often get people requesting an AI solution that is easy, fast, all-knowing and transactional. They also want to know the solution is safe, with guard rails to prevent it operating outside of the intended scope. Transparency and control are critical to meet such requirements and to comply with regulatory demands. We give companies a clear view into how their systems operate and make sure the data stays within their environment. For industries with strict rules, like healthcare or finance, we offer deployment options that meet even the most rigorous requirements, including on-premises setups if needed. Every case is different and we use different setups, different configurations, and different cloud vendors for different requirements. The key point for enterprises is that proven, production-ready solutions already exist. Success or failure rather lies in getting the implementation right which is why experience plays a big role, something that is in high demand but low supply right now. What advice would you offer companies that are just beginning to explore this technology? Don't try to do everything at once. Look for a use case that can demonstrate success quickly and then expand from there into more use cases. Instead of an 18-month overhaul, consider a four-week proof of concept to quickly deliver results and expand from there. Your customers, agents and your project will benefit faster and those quick results will get you more internal buy-in, and perhaps budget, to continue expanding. AI agents will soon outnumber human employees, with each person managing multiple digital assistants. In this new landscape, it's crucial to implement future-proof, scalable solutions rather than isolated point-to-point systems that lack seamless integrations. Any final thoughts? Large language models are improving rapidly while costs plummet – new advances emerge every month. I'm excited for a future where personal AI assistants handle everyday tasks like booking restaurants and scheduling appointments through our mobile devices or wearables. By learning our habits and accessing our calendars, these assistants will simplify our lives. However, this shift also brings challenges for businesses. Imagine a customer telling their AI agent to call their insurance company, which is using its own AI to answer. As human-to-AI and AI-to-AI interactions become common, companies must adapt quickly to evolving customer experiences and rethink processes for the AI first era.
Tahawul Tech
04-06-2025
- Business
- Tahawul Tech
'We are creating the best salespeople on the planet through conversational AI.' – Daniel Wagner, CEO of Rezolve AI
CNME Editor Mark Forker sat down with British tech entrepreneur Daniel Wagner, to find out how his company Rezolve AI is harnessing the power of conversational AI to completely transform and revolutionise customer experiences in digital commerce. It has been said that the UAE doesn't just imagine the future, it builds it. The same accusation could be levelled at Daniel Wagner, Chairman and CEO of Rezolve AI. Wagner is a serial tech entrepreneur, who has a proven track record of building hugely successful technology companies since beginning his career back in the 1980's. He first came to prominence when he created MAID in 1984, which was one of the world's first online information services, and he eventually took the company public in 1994, whilst it was also listed on the NASDAQ in 1995. In 2001, he launched another hugely successful venture with the creation of Venda, which was a cloud-based enterprise-class commerce platform that he eventually sold to US IT behemoth Oracle. In 2003, he co-founded Attraqt, which was an e-commerce software company that he again took public on AIM in 2014, and sold to a private equity firm in 2019. Wagner was one of the first to recognise the benefits of packaging electronic information and data back in 1984, and 32 years later he could see the potential of conversational AI in providing a human touch to the digital commerce space. Wagner began the conversation highlighting the factors that led to the company's inception. 'When I started Rezolve, I was obviously bringing decades of experience, knowledge and an intrinsic understanding of how commerce and search all worked. It didn't come out of nowhere, the company was born out of my deep understanding of natural language processing, and my objective was very clear, and that was we wanted to create the best salesperson on the planet by leveraging the power of conversational AI. Instead of trying to create a wide, generic solution like ChatGPT we created a platform that was specifically tailored to enable online retailers to create the best salespeople in the world,' said Wagner. Wagner pointed to the main crux of the problem for online retailers and outlined how they are trying to resolve the problem pardon the pun. 'Look, when you go online to buy items, 70% of people drop out of the process. That is a huge number. However, when you go into a physical store then 70% of people buy something, so there is something fundamentally wrong in the online shopping experience. It is completely reversed and the difference between one or the other is 70%. The reason for that is very obvious. For example, if I ask my wife to go and buy me a mobile phone, she couldn't do it online because she doesn't know what an OLED screen is, she doesn't know what a MB is, she doesn't know the difference between iOS and Android. She doesn't know any of these things, but in an online environment then that's how these products would be presented to her. But if she went into a mobile phone shop and said, 'I need to buy a phone for my husband,' one or two questions would be asked of her, and then the salesperson would be able to sell her a phone,' said Wagner. Wagner stressed that the business model of Rezolve AI is engineered towards empowering online retailers to create what he described as the 'perfect salesman' through the use of natural language processing. 'We've developed our own language model, and it's essentially like a salesperson ready to learn about the retailer's products. That salesperson has sales techniques—it's trained on how to sell and close sales, how to discuss products and engage with customers in a way the very best human salesperson would in a physical store. We know it's going to be a market leader, and Google and Microsoft have also recognised that what we have is totally unique in this space, and that's why they have partnered with us. We haven't just jumped on the AI bandwagon either, we have been working on this since 2016 and have invested $130m during that period,' said Wagner. Wagner also added that Rezolve AI has a language model with products layered on top that allows them to go to any retailer in the world. 'We support 96 languages, so we can work with retailers in Arabic, Korean, Chinese, French, Spanish etc, and they can engage with us in their own language, and that is an extremely powerful capability to have when you are trying to humanise that online digital experience. We are levelling up commerce and that's the way I see it,' said Wagner. One of the many key differentiators of what Rezolve AI does is in relation to its ability to tackle hallucinations. 'There are plenty of companies out there in the marketplace saying they're providing AI solutions that allow some sort of conversational engagement with retail. However, crucially most of it is around customer service. When you're dealing with a product catalogue, there are real problems with Gen AI. If I'm a cosmetics retailer with 200 products, and say there is 100 fragrances for men and 100 for women, then these products could have names like 'Sauvage' and 'Beast.' The issue is the descriptions may say things like its smells like 'blackberries with sandalwood notes', but Gen AI doesn't understand that. There's no context. So, it hallucinates and gives wrong or nonsensical answers. At the end of the day, the propensity for hallucinations goes way up. Normally, Gen AI hallucination rates are about 3–4%. But with product catalogues, it's about 17%. We have spent nine years solving this hallucination problem in product catalogues, and that's what makes us really unique,' said Wagner. Wagner reiterated the importance and significance of their partnership with Google and Microsoft in terms of the market credibility it gives Rezolve. 'We have three patents on how we do what we do, and ultimately that's why tech giants such as Microsoft and Google are partnering with us. Microsoft is a massive leader in AI, and Google is the number one search company in the world—they're not going to partner with us unless we have something special, and we do. They're selling us into their customer base. We've got videos and references from both saying this, and crucially, we're the only ones who've been focused on solving this specific problem for the last nine years and that gives us a huge advantage,' said Wagner. The success of Rezolve AI since beginning to roll out their platform at the start of the year has been nothing short of staggering. 'The company went public in August 2024, but in April 2025, we got publicly listed on the NASDAQ after processing $50bn in sales through our technology in just the first 3 months of us going live with our platform. We started in January, before that we had almost no revenue. As the old saying goes, the numbers don't lie, and they certainly don't in our case. The success we have had is unprecedented really, and only serves to further reinforce how unique we are, and how we are going to be the global market leader in this space. From a standing start, over 41 million consumers now have our technology on their phones, in their apps. And we've improved conversion rates by 25% on the sites where we've been deployed, and that is astronomical,' said Wagner. Wagner said they are excited about the opportunities emerging all across the Middle East. He said the company is laying solid foundations in the Gulf, and hopes to be in a position very soon to announce a very significant customer in the region. When asked was he preparing the company to ultimately be acquired by a large tech company, Wagner insisted that they were only at the beginning of the journey. 'Look in all reality, I don't need to do this, I've had a hugely successful career. However, the fact remains that I am driven by a desire to build products that can totally transform and revolutionise industries. In relation to the question of us being acquired, it's a fair question. In the 1980s, I created the world's first digital information businesses, and then in the late 1990s and early 00s, I built one of the first cloud-based commerce platforms – and both of those were acquired by Oracle. Now, I'm building the future of retail interaction, but I see this as my career's pinnacle, and I'm totally energised by it all, and trust me, this is just the beginning of the journey,' said Wagner.
TechCrunch
03-06-2025
- Automotive
- TechCrunch
Learn how Toyota and NLX successfully partnered at TC Sessions: AI
We're down to the final two days before TechCrunch Sessions: AI unites the broader AI community at UC Berkeley's Zellerbach Hall on Thursday, June 5! Expect a packed agenda with top speakers, expert panels, top-tier networking, and Side Events from our partners. One of our speakers is Andrei Papancea, CEO and co-founder of NLX, who will share insights from the company's successful partnership with Toyota — focused on building AI-powered conversational experiences for car repair. With the event just days away, now's your last chance to lock in serious savings. Here's how: Save $300 when you register solo. Bring a guest and get 50% off their ticket with our 2-for-1 deal. Want an even better deal? Play our AI trivia challenge for a chance to score two tickets for just $200 total. So, without further ado, meet Papancea and get to know his insightful session on the main stage. Get to know Andrei Papancea Papancea is currently the CEO of NLX, a company that has raised more than $25 million in funding to fuel its mission of providing automated customer service experiences to companies ranging from United Airlines to Red Bull. Its efforts are built on Papancea's prior experiences building American Express's Conversational AI platform, which has become a primary point of contact for customer experiences with the company. Papancea and Toyota join the main stage For his session, Papancea will dive into NLX's partnership with Toyota, which centers on AI-powered conversational experiences tailored specifically for car repair. The initiative gives technicians instant access to a vast knowledge base — drawn from millions of pages of repair guides, manuals, diagrams, and other highly specific resources — to help them diagnose and complete repairs more efficiently. In doing so, Toyota has already been able to improve a core KPI: the productivity of its technicians. And that's exactly the kind of topic we're looking to dive into at TC Sessions: AI, one that demonstrates the real-world impact of AI implementation and partnerships. Techcrunch event Save now through June 4 for TechCrunch Sessions: AI Save $300 on your ticket to TC Sessions: AI—and get 50% off a second. Hear from leaders at OpenAI, Anthropic, Khosla Ventures, and more during a full day of expert insights, hands-on workshops, and high-impact networking. These low-rate deals disappear when the doors open on June 5. Exhibit at TechCrunch Sessions: AI Secure your spot at TC Sessions: AI and show 1,200+ decision-makers what you've built — without the big spend. Available through May 9 or while tables last. Berkeley, CA | REGISTER NOW Join our full lineup of insightful AI sessions featuring leaders from Google Cloud, Amazon, TwelveLabs, OpenAI, Anthropic, and many more. Check out the full speaker list and agenda. Your last chance to join TechCrunch Sessions: AI with real ticket savings TechCrunch Sessions: AI is just days away — don't miss your chance to explore practical AI solutions, real-world applications, and top-tier networking with fellow experts and innovators. Take advantage of last-minute deals: Save up to $300 on single tickets, bring a guest with our 2-for-1 offer, or win a special promo code through our AI Trivia Challenge to get two tickets for just $200. All low rates and special offers disappear when the doors open on June 5 — secure your pass today and join us at UC Berkeley's Zellerbach Hall!
Geeky Gadgets
02-06-2025
- Business
- Geeky Gadgets
ElevenLabs Introduces New Multimodal Conversational AI
What if your next interaction with a virtual assistant felt as natural as chatting with a friend? Imagine asking a question aloud, seamlessly switching to typing a sensitive detail like your email address, and receiving an instant, lifelike response in your preferred language. This isn't science fiction—it's the promise of multimodal conversational AI, a new advancement that's transforming how we communicate with technology. By combining text and voice inputs with unparalleled precision, this innovation bridges the gap between human and machine, offering a fluid, intuitive experience that adapts to your needs in real time. It's not just about convenience; it's about redefining what's possible in human-AI interaction. ElevenLabs introduce how its innovative system is setting a new standard in conversational AI. You'll discover the power of speech-to-text and text-to-speech technologies, the innovative potential of multilingual capabilities, and the security measures that make handling sensitive information more reliable than ever. Whether you're curious about its real-world applications, such as AI-powered customer service, or intrigued by its seamless integration into business platforms, this journey will reveal how multimodal AI is reshaping communication. As we delve deeper, consider this: could this technology be the key to bridging global divides and enhancing human connection in an increasingly digital world? Multimodal Conversational AI Overview The Importance of Multimodal Functionality The defining feature of this conversational AI is its multimodal functionality, which allows users to switch effortlessly between text and voice inputs. This capability enhances user convenience and ensures a more personalized interaction. For example: You can start a conversation by speaking and then type sensitive information, such as an email address or credit card number, to ensure accuracy and privacy. This dual-input approach minimizes transcription errors, making it particularly effective for handling critical data. By combining flexibility and precision, the system delivers a more reliable and user-friendly communication experience. This adaptability is especially valuable in scenarios where accuracy and efficiency are paramount. Advanced Speech-to-Text and Text-to-Speech Technologies At the core of this system are its speech-to-text (STT) and text-to-speech (TTS) technologies, which work in tandem to create a natural and fluid conversational experience: Speech-to-Text: This component accurately transcribes spoken words into written text, allowing the AI to process voice commands with precision. This component accurately transcribes spoken words into written text, allowing the AI to process voice commands with precision. Text-to-Speech: It converts written responses into lifelike audio, making sure a more human-like interaction for users. These technologies ensure clarity and responsiveness, whether users are engaging in real-time conversations or relying on automated responses. By bridging the gap between text and voice communication, the system provides a more intuitive and engaging experience. ElevenLabs Multimodal Conversational AI Watch this video on YouTube. Browse through more resources below from our in-depth content covering more areas on multimodal conversational AI. Breaking Language Barriers with Multilingual Capabilities One of the standout features of this conversational AI is its multilingual support, which includes over 32 languages. This capability enables businesses to connect with a global audience and overcome language barriers effectively. Key benefits include: Accurate comprehension and responses in widely spoken languages such as English, Spanish, and Mandarin, among others. Improved customer engagement for global enterprises operating across diverse regions. By facilitating seamless communication in multiple languages, the system enables businesses to expand their reach, enhance customer satisfaction, and build stronger relationships with international clients. Seamless Integration for Business Applications Designed with businesses in mind, this AI system integrates effortlessly into existing infrastructures. Its compatibility with widely used communication platforms, such as Twilio and SIP trunking, ensures straightforward deployment across various industries. Common applications include: Customer service Sales and lead generation Technical support This flexibility allows businesses to tailor the AI to their specific operational needs, streamlining communication processes and improving overall efficiency. By reducing the workload on human agents, the system also helps optimize resource allocation. Customizable Setup for Diverse Requirements The system's configurable setup ensures adaptability to a wide range of technical requirements. Businesses can choose from several integration options, including: Widgets for quick implementation SDKs for custom application development WebSocket for real-time communication Comprehensive documentation simplifies the setup process, even for complex configurations. This level of customization ensures the AI aligns with unique workflows, maximizing its effectiveness in real-world applications. Whether for small businesses or large enterprises, the system's versatility makes it a valuable asset. Prioritizing Accuracy and Security Accuracy and security are critical components of this conversational AI. By allowing users to type sensitive information, such as personal details or order numbers, the system minimizes transcription errors and ensures data integrity. This feature is particularly beneficial in scenarios requiring precision, such as: Processing refunds and returns Verifying customer identities By addressing these challenges, the system provides secure and reliable interactions for both users and businesses. This focus on accuracy and security enhances trust and reduces the risk of errors in critical processes. Real-World Applications: AI-Powered Refund Agent A practical example of this technology is its use as an AI-powered refund agent. Consider a scenario where a customer requests a refund: The AI processes the order number and verifies the email address provided by the customer. If necessary, it seamlessly switches languages to accommodate the customer's preference. The system resolves the issue quickly, reducing the workload on human agents and making sure customer satisfaction. By using its multimodal and multilingual capabilities, the AI delivers faster resolutions while maintaining professionalism and accuracy. This application highlights the system's potential to enhance operational efficiency and improve customer experiences. Setting a New Benchmark in Conversational AI The multimodal conversational AI system from ElevenLabs represents a significant advancement in artificial intelligence. By combining text and voice input processing, advanced language models, and seamless business integration, it offers a versatile solution for enhancing communication. Key advantages include: Handling sensitive information with precision and reducing errors. Supporting multiple languages to connect with a global audience. Integrating effortlessly with existing platforms for streamlined operations. Whether you aim to improve customer service, optimize business processes, or provide a more natural conversational experience, this technology establishes a new standard for AI-driven communication. Its adaptability and reliability make it a powerful tool for businesses looking to stay ahead in an increasingly connected world. Media Credit: ElevenLabs Filed Under: AI, Top News Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
