Voice Assistant In Customer Experience

Voice AI is more than just plugging in an LLM. It's an orchestration challenge involving complex AI coordination across STT, TTS and LLMs, low-latency processing, and context & integration with external systems and tools. Let's start with the basics: ---- Real-time Transcription (STT) Low-latency transcription (<200ms) from providers like Deepgram ensures real-time responsiveness. ---- Voice Activity Detection (VAD) Essential for handling human interruptions smoothly, with tools such as WebRTC VAD or LiveKit Turn Detection ---- Language Model Integration (LLM) Select your reasoning engine carefully—GPT-4 for reliability, Claude for nuanced conversations, or Llama 3 for flexibility and open-source options. ---- Real-Time Text-to-Speech (TTS) Natural-sounding speech from providers like Eleven Labs, Cartesia or Play.ht enhances user experience. ---- Contextual Noise Filtering Implement custom noise-cancellation models to effectively isolate speech from real-world background noise (TV, traffic, family chatter). ---- Infrastructure & Scalability Deploy on infrastructure designed for low-latency, real-time scaling (WebSockets, Kubernetes, cloud infrastructure from AWS/Azure/GCP). ---- Observability & Iterative Improvement Continuous improvement through monitoring tools like Prometheus, Grafana, and OpenTelemetry ensures stable and reliable voice agents. 📍You can assemble this stack yourself or streamline the entire process using integrated API-first platforms like Vapi. Check it out here ➡️https://bit.ly/4bOgYLh What do you think? How will voice AI tech stacks evolve from here?

🔮 Design Guidelines For Voice UX. Guidelines and Figma toolkits to design better voice UX for products that support or rely on audio input ↓ 🤔 People avoid voice UIs in public spaces, or for sensitive data. ✅ But do use them with audio assistants, learning apps, in-car UIs. ✅ Good conversations always move forward, not backwards. 🤔 The way humans speak is different from the way we write. 🤔 What people say isn’t always what they mean by saying it. ✅ First, define relevant user stories for your product. ✅ Sketch key use cases, then add detours, then edge cases. ✅ Design VUI personas: tone of voice, words, sentence structure. ✅ Listen to related human conversations, transcribe them. ✅ Write conversation flows for happy and unhappy paths. ✅ Add markers (Finally, Now, Next) to structure the dialogue. ✅ Accessibility: support shaky voices and speech impediments. ✅ Allow users to slow down or speed up output, or rephrase. ✅ Adjust speech patterns, e.g. speaking to children differently. 🚫 There are no errors or “wrong input” in human interactions. 🤔 Give people time to think: 8–10s is a good time to respond. ✅ Design for long silences, thick accents, slang and contradictions. Keep in mind that many people have been “burnt” with horrible, poorly designed automated phone systems. If your voice UX will come across even nearly as bad, don’t be surprised by a very low usage rate. You can’t replicate a long scrollable list in audio, so keep answers short, with max 3 options at a time. Instead of listing more options, ask one direct question and then branch out. Re-prompt or reframe when certainty is low. People choose their voice assistant based on the personality it conveys, and the friendliness it projects. So be deliberate in how you shape the tone, word choice and the melody of the voice. Don’t broadcast personality for repetitive tasks, but let is shine in a conversation. And: if you don’t assign a personality to your product, users will do it for you. So study how your customers speak. How exactly they explain the tasks your product must perform. The closer you get to a personal human interaction, the easier it will be to earn people’s trust. Useful resources: Voice Principles, by Ben Sauer https://lnkd.in/dQACgwue Voice UI Design System, by Orange https://lnkd.in/ezP-9QUu Designing A Voice Persona, by James Walsh https://lnkd.in/e3WXaxEC Voice UI Kit (Figma), by Shadiah Garwell https://lnkd.in/eGjJCWf7 Conversational UIs (Figma), by ServiceNow https://lnkd.in/enHVSEWP Voice UI Guide, by Lars Mäder https://vui.guide/ #ux #design

Customer service conversations are the heartbeat of your business. They are a treasure trove of data about your operation and product flows, your agents and how they treat your customers, and your customers' preferences and needs. Yet, most contact centers analyze only a fraction of these interactions, using dated technology, leaving valuable insights untapped and decisions driven by incomplete data. At Replicant, we believe it’s time to bring every conversation to light. That’s why Conversation Intelligence is transforming customer service conversations into actionable insights. By analyzing 100% of calls with the latest audio AI, leaders can identify operational issues that lead to unnecessary calls, optimize agent performance, and pinpoint automation opportunities—turning their contact centers into strategic assets. For example, a large e-commerce provider used Conversation Intelligence to uncover an issue impacting 5% of their calls. Within one week, they implemented a fix that redefined their customer service strategy, eliminating inefficiencies and elevating their customer experience. This isn’t just about solving problems; it’s about leading with clarity. When every customer conversation becomes a data point for innovation, and AI summarizes it into actions for you, your contact center becomes a competitive advantage. The future belongs to leaders who anticipate, innovate, and act boldly. Are you ready to lead the way?

🛑 𝗗𝗼𝗻’𝘁 𝗙𝗹𝘆 𝗕𝗹𝗶𝗻𝗱: 𝗨𝘀𝗲 𝗔𝗜 𝘁𝗼 𝗞𝗻𝗼𝘄 𝗬𝗼𝘂𝗿 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀 Having trouble keeping pace with your customers' desires and needs? If you're not leveraging real-time data on customer behavior and preferences, you're essentially flying blind. 💥 This lack of insight can cripple your marketing and sales efforts, leading to ineffective customer engagements and stunted sales growth. Here’s where Voice AI steps in as a powerful ally: ❇️ Real-Time Data Collection: Implement Voice AI to engage with customers directly. This technology collects essential data on preferences, concerns, and feedback as the conversation happens. ❇️ Instant Feedback Loop: Set up your Voice AI to provide real-time feedback to your marketing and sales teams. This means they can pivot and adjust strategies instantly, enhancing the effectiveness of your campaigns on the fly. ❇️ Real-Time Alert System: Integrate a real-time alert system within your Voice AI setup. This can notify team members immediately when it detects key customer triggers, like expressions of dissatisfaction or excitement, prompting swift and appropriate action. By integrating these strategies, you'll not only meet but exceed customer expectations, enhancing engagement and driving sales. How are you leveraging technology to stay on top of customer preferences? Share your strategies below! #innovation #digitalmarketing #technology #bigdata #entrepreneurship #voiceai

Customer conversations are full of signals. The trick is knowing where to look. Voice data gives teams a clearer view into what customers need and how they’re feeling. When you can spot patterns in those conversations, it gets a lot easier to respond faster, coach more effectively, and deliver a more consistent experience. Here are three insights we’ve seen really move the needle for our customers: 🔹 Why customers are calling: Understanding common call drivers helps you anticipate needs, improve self-service, and reduce repeat issues. 🔹 Which moments carry risk: Things like escalations, interruptions, or sudden tone shifts can point to where customers might be getting stuck or frustrated. 🔹 Where to focus coaching efforts: Voice data can highlight exactly where reps need support, whether it’s navigating objections, adjusting tone, or wrapping things up with confidence. If you had a clearer view into your voice data, what insights would you want to uncover first?

Why do companies use IVR (Interactive Voice Response) systems? Maybe it was to automate those overly mundane calls so that companies didn’t have to answer said calls with a more-expensive human agent. Perhaps we, consumers like you and me, drove that corporate decision by demanding an automated solution that wouldn’t require an agent. Either way, the root cause was to provide the caller -- you and me -- with a self-service option. But what happens when callers get stuck in an infinite loop they can't get out of? 🫣 This just happened to me today, so here's some advice: Only cross-functional teams should develop scripts – Involve people from the Contact Center, IT, etc. to ensure what you want to do is actually possible. Also, think about who can be involved to ensure that the system supports your company's brand. Finally, if you can tap into caller input at this point, don't be shy. Put the choices in logical order — In most cases, the choices should start with the item that callers select most often. This minimizes the caller’s time listening for their choice and toll-free service cost. It's a healthy diet for everyone. Test new scripts – I hate to say it, but every new script should be tested in a variety of ways. For example: - Does each choice take the caller to the correct destination? - Are we using industry jargon people aren't familiar with? - When Bill presses 1 after entering all his details, is then told to hold for an Agent, only to get another recording that says to input all your details again and hold for an Agent, is that a good thing? Testing can be expensive. My solution? Use your employees' family members to test and maybe buy them lunch via Uber Eats. Above all else, listen to the feedback and make the the changes before putting the script on line. This isn't a “pride of authorship” moment. If your script sucks, just change it. Test regularly — It isn't enough to test a new script at implementation. Test monthly to ensure scripts continue to work properly, route to the desired agent/group, and still make sense for the business and your customer. What else would you add here? What am I missing? #customerexperience #IVR #contactcenter #callcenter

If you are an organisation using AI or you are an AI developer, the Australian privacy regulator has just published some vital information about AI and your privacy obligations. Here is a summary of the new guides for businesses published today by the Office of the Australian Information Commissioner which articulate how Australian privacy law applies to AI and set out the regulator’s expectations. The first guide is aimed to help businesses comply with their privacy obligations when using commercially available AI products and help them to select an appropriate product. The second provides privacy guidance to developers using personal information to train generative AI models. GUIDE ONE: Guidance on privacy and the use of commercially available AI products Top five takeaways * Privacy obligations will apply to any personal information input into an AI system, as well as the output data generated by AI (where it contains personal information).  * Businesses should update their privacy policies and notifications with clear and transparent information about their use of AI * If AI systems are used to generate or infer personal information, including images, this is a collection of personal information and must comply with APP 3 (which deals with collection of personal info). * If personal information is being input into an AI system, APP 6 requires entities to only use or disclose the information for the primary purpose for which it was collected. * As a matter of best practice, the OAIC recommends that organisations do not enter personal information, and particularly sensitive information, into publicly available generative AI tools. GUIDE 2: Guidance on privacy and developing and training generative AI models Top five takeaways * Developers must take reasonable steps to ensure accuracy in generative AI models. * Just because data is publicly available or otherwise accessible does not mean it can legally be used to train or fine-tune generative AI models or systems.. * Developers must take particular care with sensitive information, which generally requires consent to be collected. * Where developers are seeking to use personal information that they already hold for the purpose of training an AI model, and this was not a primary purpose of collection, they need to carefully consider their privacy obligations. * Where a developer cannot clearly establish that a secondary use for an AI-related purpose was within reasonable expectations and related to a primary purpose, to avoid regulatory risk they should seek consent for that use and/or offer individuals a meaningful and informed ability to opt-out of such a use. https://lnkd.in/gX_FrtS9

New research from MarktechPost reveals voice AI's significance in business strategy despite persistent challenges with current tech. While a remarkable 67% of businesses consider voice AI core to their product and business strategy, only 21% report being "very satisfied" with their current voice agent technology. This satisfaction gap is driving the next wave of development, with 84% of organisations planning to increase their voice tech budgets in the coming year. The research shows early adopters are moving quickly—15% of organisations are already actively developing advanced voice agents, with nearly all planning deployment within 12 months. As these more sophisticated systems reach the market, they promise to transform the customer experience beyond today's basic command-response implementations. Article: https://lnkd.in/d2jTwe4f #VoiceAI #AI #SpeechTechnology

Dear Brand Leaders, Voice AI agents are here. Great news for customers, but making decisions for your business can be a complex process. Identifying the RIGHT partner isn't always easy. Here's what I recommend... Based on my experience as a buyer of emerging technologies, speaking at many events, meeting vendors, and hearing their roadmaps, ask solution providers the following questions: ⁣ ①Can the technology instantly access a customer's full history across all systems, so our team has the necessary information, and customers never need to repeat themselves? ② Does it track tone, sentiment, and emotion during every interaction, so our teams can detect frustration early and adjust in real time? ③ Can we measure the impact on resolution time, customer effort, and employee experience to improve results? I’m sharing 10 more must-ask questions in my upcoming blog article. (👇link in comments) ⁣ Most importantly....No silo decision making! To create exceptional Customer eXperiences, bring cross-functional teams together from the start and define the requirements collaboratively. ✓Your IT friends know what the systems can and can’t do. ✓Your operations team knows where the process breaks. ✓Your marketing team protects the brand promise. ✓Your CX and frontline teams know where customers get angry. Approaching this non-negotiable is crucial; otherwise, you'll spend more in the long run and risk losing the very people you aim to serve. ⁣ That's Doing CX Right®. ❤️Stacy⁣ Doing CX Right®‬ #VoiceAI #CustomerExperience #DigitalTransformation⁣

I just built a Voice RAG Agent one that can listen, think, and talk back using your own data. Instead of typing prompts into ChatGPT, imagine being able to call an AI agent, ask a question like: “What does HIPAA say about contingency planning?” and get a clear, conversational voice answer pulled directly from your company’s documents. Here’s what powers it : 🔹 Retell AI - handles the real-time voice conversation 🔹 n8n - automates the workflow between tools 🔹 OpenAI embeddings & Pinecone - make it a true RAG system that retrieves answers from your own files Where this can be useful: – Compliance hotlines (HIPAA, SOC2, ISO, etc.) – Customer support that speaks your internal policy docs – Voice-based knowledge assistants for internal training – Product documentation helplines that talk to clients This isn’t just another chatbot it’s a voice-first AI system that learns from your content, not the public web. Watch the full tutorial below to see how it’s built step-by-step using Retell AI + n8n + OpenAI.