AI Wine Expert Voice Assistant

@AwaisChaudhry

Hi there, I’m Muhammad Awais, and I’ll build your wine expert voice assistant as a smooth, cross‑platform conversational partner that greets visitors, uncovers their taste profile, and suggests bottles for meals or special occasions. The system will switch naturally between spoken and text modes, with real-time speech-to-text and text-to-speech in a brand‑appropriate voice, plus a memory layer so later turns refine past suggestions. I’ll connect to your wine catalog via Swagger REST/GraphQL, and choose a solid NLU/NLG stack (OpenAI or Rasa) with a robust orchestration layer (Dialogflow CX or equivalent). Deliverables include the conversation flow and intent/entity schema, fully coded agent with API hooks, web and mobile SDKs, an admin/config file to tweak prompts and fallbacks, test scripts with a recorded demo, and a deployment/knowledge-transfer session. I’ll meet the acceptance targets: first response latency under 2 seconds for text and under 3 seconds for voice, 90% intent recognition on your tests, 4/5+ recommendation relevance by a panel, and zero uncaught errors in 50 turns. If you’ve shipped voice experiences in the beverage space, we’ll hit the ground running. Which NLU/NLG stack and licensing do you want us to use (OpenAI, Rasa, etc.)? What is your expected peak user load and any additional latency targets beyond the stated 2-3 seconds? Can you share the Swagger specs for your wine catalog API and authentication method? Do you want cloud-only processing or shou

@leadconcept

Hello, I understand you need a conversational “digital sommelier” that works seamlessly across web and mobile, handling both voice and text, gathering user taste preferences, and delivering context-aware wine recommendations while maintaining natural dialogue and real-time responsiveness. A few questions to clarify before starting: 1, Do you have a preference for the NLU/NLG engine (OpenAI, Rasa, Dialogflow CX), or should our team recommend the most scalable approach? 2, Should the TTS voice match a specific style or accent for brand consistency? 3, Do you want the agent to learn and improve over time with usage data, or stay deterministic per session? Our team has extensive experience building multi-platform conversational agents with voice and text modalities, integrating real-time APIs, and crafting recommendation engines for hospitality and e commerce clients. We can handle full end-to-end delivery, including flow design, entity/intents schema, API integration, SDK components for React and Flutter, admin configuration, and robust test scripts. The current bid amount is a placeholder to submit this proposal. Regards Yasir LEADconcept PS: Let me know, if you want to see our team past work to determine our skills/expertise or past customer's references.

@Julian29

Hello, HAVE HANDS-ON EXPERIENCE WITH SUCH PROJECT I have 11+ years of proven experience in building AI-driven conversational applications and confidently understand your requirement. The goal is to create a digital sommelier that provides real-time, natural voice and text recommendations, remembers user preferences, and integrates seamlessly into web (React) and mobile (Flutter) platforms. I will work full time, 40 hours/week for you. *** Natural-language understanding and generation for voice and text with context memory *** Real-time recommendation engine linked to your wine catalog via REST/GraphQL *** STT/TTS integration with pleasant brand-appropriate voice and low-latency responses -->> Modular, maintainable architecture with clear API hooks and configurable admin controls -->> AI orchestration combining NLU/NLG, recommendation logic, and fallback handling -->> Clean, production-ready code with demo scripts, test plans, and deployment guide I have a few questions regarding wine catalog schema and preferred TTS provider to clarify in chat before starting. Thanks Julian

@neerajdhameliya

Hello, I have experience building end-to-end conversational AI agents, including voice-enabled ones for recommendation systems (eCommerce and lifestyle apps). I’ve shipped projects using: -> OpenAI / Anthropic for NLU & NLG -> ElevenLabs or Google Cloud for high-quality, brand-friendly TTS (and Whisper/STT for speech) -> RAG + memory layers to keep context across turns -> Fast API integrations (REST/GraphQL) to pull live product data -> React + Flutter integration with WebSockets for real-time feel -> Simple admin tools (config files or basic UI) to tweak prompts, voice, and fallbacks I can deliver all your listed items: conversation flows/schema, full coded agent with your wine catalog API (using your Swagger docs), SDK components for web/mobile, admin config, test scripts + demo recordings (text & voice), and a clear deployment guide + handover session. I focus on low latency (<2s text, <3s voice), high intent accuracy, and relevant recommendations. I’ve built several voice agents before, so I understand the natural, expert tone needed here. I can help make your digital sommelier feel like a real wine pro, friendly, knowledgeable, and adaptive. I’m happy to hop on a quick call to discuss details and share work samples. Looking forward to hearing from you! Best regards, Niral

@tangramua

Dear ,   We carefully studied the description of your project and we can confirm that we understand your needs and are also interested in your project. Our team has the necessary resources to start your project as soon as possible and complete it in a very short time.   We are 25 years in this business and our technical specialists have strong experience in Audio Services, Mobile App Development, iPhone, Voice Talent, AI (Artificial Intelligence) HW/SW, Voice Over, Natural Language Processing, AI Chatbot Development and other technologies relevant to your project.   Please, review our profile https://www.freelancer.com/u/tangramua where you can find detailed information about our company, our portfolio, and the client's recent reviews.   Please contact us via Freelancer Chat to discuss your project in details.   Best regards, Sales department Tangram Canada Inc.     

@ixorawebmob

Hello, I’ve gone through your project details on the AI Wine Expert Voice Assistant, and this is something I can definitely help you with. With over 10 years of experience in mobile and web app development, particularly in AI and voice technology, I can create a conversational digital sommelier that seamlessly switches between voice and text. I focus on clean architecture and scalable solutions, ensuring a smooth user experience. I’ve successfully developed voice agents in the past, including those in the food and beverage space. I understand the need for natural language processing, real-time responses, and a robust recommendation system based on user preferences. I will be happy to leverage technologies like OpenAI or Rasa along with suitable speech services to achieve your goals. Here is my portfolio: https://www.freelancer.in/u/ixorawebmob I’m interested in your project and would love to understand more details to ensure the best approach. Could you clarify: Are there any specific dietary restrictions or preferences that should be considered in the recommendations? Let’s discuss over chat! Regards, Arpit Jaiswal

@kareemmaize

Hi, there. The hardest part of a digital sommelier isn’t recommendations—it’s maintaining fast, natural dialogue across voice and text while preserving context. I’ve seen agents miss SLAs when STT/TTS and catalog calls aren’t orchestrated carefully. I’d design a lightweight orchestration layer with deterministic turn memory, using an LLM for NLU/NLG, a proven STT/TTS provider, and a rules-assisted recommender that blends preferences, food pairing, and occasion from your live catalog. Prompts and fallback behavior stay configurable, not hard-coded. Licensing would be transparent up front (LLM, STT/TTS usage-based), with clean SDK hooks for React and Flutter. I’ll validate latency early and stress-test long conversations. Do you want recommendations constrained to in-stock items only, or allowed to suggest alternatives?

@rbtech1984

Hello There!!! ⚜️⭐⭐⭐⭐⚜️(( AI-powered digital sommelier delivering voice and text recommendations in real time ))⚜️⭐⭐⭐⭐⚜️ I see you are building a conversational wine assistant that guides users with personalized bottle suggestions, taste profiling, and contextual advice across web and mobile platforms. The aim is a natural, human-like dialogue experience with intelligent recommendations and seamless voice/text interaction. I bring experience building AI voice agents with NLU/NLG, speech-to-text/text-to-speech pipelines, recommendation engines, and multi-platform integration (React web, Flutter mobile). I focus on production-ready systems with low-latency responses and robust conversation memory. Three key features: Real-time voice & text conversation with brand-appropriate TTS/STT Context-aware wine recommendation engine with food and occasion pairing Prior-turn memory and graceful fallback/escalation handling I can deliver the coded agent, SDK integrations, admin configuration, test scripts, and a demo, ensuring your team can maintain and expand it smoothly. Let’s discuss how we can make your digital sommelier delight your users. Warm Regards, Farhin B.

@xuezhe315

Hello, I can help you build a production-ready conversational assistant that works seamlessly across web and mobile, switching naturally between voice and text while refining recommendations in real time as it learns a user’s palate, meal context, and occasion. My approach centers on conversation quality and engineering rigor: Unified conversational core supporting text and voice with shared state, so spoken and typed turns stay perfectly in sync Intent + entity design tailored to wine discovery (taste profiles, regions, varietals, pairings, aging) rather than generic chat flows Context-aware recommendation engine that blends user preferences, food pairing logic, and live catalog data via your REST/GraphQL APIs Low-latency STT/TTS pipeline using a brand-appropriate voice, with careful orchestration to meet sub-3s voice response targets Memory across turns to refine suggestions without repetition, plus graceful fallbacks and clean human handoff I’m comfortable integrating this into a React web frontend and Flutter mobile app, and selecting the right mix of NLU/NLG, orchestration, and speech services based on cost, latency, and licensing transparency. Deliverables would include clearly documented conversation flows, a fully coded agent with database hooks, SDK-ready integration components, configurable prompts and voices, test scripts, recorded demos, and a deployment + knowledge-transfer walkthrough.

@codefusion53

Hi, I am an AI and full-stack developer with experience building conversational voice agents integrated into web and mobile apps. I can deliver a production-ready “digital sommelier” capable of fluid voice and text interactions, personalized wine recommendations, and memory of prior conversations. Using a stack combining OpenAI or Rasa for NLU/NLG, Dialogflow CX or equivalent for orchestration, and a high-quality TTS/STT service like ElevenLabs or Amazon Polly, I will connect the agent to your wine catalog via API and ensure low latency responses, high intent accuracy, and seamless multi-turn conversations. Deliverables include schema and flow design, fully coded agent, SDK integration for React and Flutter, admin controls for prompts and voice, test scripts, recorded demos, and a deployment guide for your team. I focus on scalability, accuracy, and a natural, brand-consistent user experience. Best, Justin

@shanec35

Hello, I have carefully analyzed your requirements and previously delivered an end-to-end conversational voice and text assistant for a consumer brand that ran on web and mobile. It combined real-time STT/TTS, context-aware recommendations from a live product catalog, and multi-turn memory so users could refine preferences naturally without repeating themselves. Your project requires a fluid voice-and-text experience, low-latency responses, strong intent recognition, and real-time wine recommendations tied to your API. I will design a clear intent and entity schema, integrate a proven NLU stack with high-quality TTS/STT, maintain conversational memory across turns, and connect the recommendation engine directly to your REST or GraphQL catalog, with graceful fallbacks and human escalation. I am available to begin work immediately and am committed to delivering a production-ready digital sommelier that meets your performance targets and feels genuinely human, within the shortest possible timeframe. Thank you, Elenilson

@shushmobiweb6

Hello I have carefully reviewed your plan for an AI-powered digital sommelier that works seamlessly across web and mobile. You are aiming to create a conversational assistant that speaks and chats naturally, understands evolving taste preferences, and delivers intelligent wine recommendations tied directly to your live catalog—and that’s exactly the type of system I love building. I have 7+ YEARS OF EXPERIENCE in conversational AI, voice assistants, and recommendation systems, I have delivered production-ready solutions that combine NLU/NLG, STT/TTS, and real-time API integrations. I can confidently manage this project from initial architecture and conversation design through full implementation, testing, and final deployment. What I’ll ensure: → NATURAL VOICE + TEXT conversations with shared memory → Smart wine recommendations using preferences, food pairing, and occasion context → FAST RESPONSE TIMES with reliable, scalable backend logic → Graceful fallbacks and smooth human escalation I am genuinely excited about crafting a wine assistant that feels like speaking to a real sommelier—polished, knowledgeable, and adaptive. You will get UNLIMITED UI REVISIONS UNTIL SATISFACTION, post-launch support, and clear documentation for your team. I will work full time 40 hours/week for you and stay fully committed to quality and performance. Sushma

@khurshid040462

Hi there, I'm excited about the opportunity to work on your AI Wine Expert Voice Assistant project. With extensive experience in building conversational agents and a thorough understanding of Natural Language Processing (NLP) and Machine Learning technologies, I can create a sophisticated digital sommelier that delivers personalized wine recommendations seamlessly. My approach will ensure that the voice and text interactions feel natural and engaging, enhancing the user experience. I propose using a combination of OpenAI for natural language understanding and generation, powered by a robust recommendation engine, and integrating it within your React and Flutter environments. Let's discuss further how I can bring your vision to life with a timeline for initial deliverables. Best regards,

@chiragardeshna

Hi there, I have thoroughly reviewed your project AI Wine Expert Voice Assistant. I have built production-grade conversational agents that combine voice and text with real-time recommendations and long-lived conversational memory. I have 7+ years of experience handling AI assistants using OpenAI or Rasa-style NLU, real-time STT/TTS pipelines, context-aware recommendation engines, and clean API integrations with live catalogs. I regularly deploy agents inside React web apps and Flutter mobile apps with low-latency response targets. I reduce client risk by designing explicit intent schemas, scoped fallback paths, and modular AI services so voice, chat, and recommendations evolve independently. I document flows, configs, and deployment clearly so your team can tune prompts, voices, and escalation logic safely. I am available to start immediately. Regards Chirag

@MIndlabsAi6

Hello sebastian2484, I am Vishal Maharaj, with 20 years of experience in AI Chatbot Development and Mobile App Development. I have carefully reviewed your project requirements for the AI Wine Expert Voice Assistant. To achieve the desired functionality, I propose utilizing a combination of OpenAI for NLU/NLG, Dialogflow CX for orchestration, and Amazon Polly for TTS/STT services. The core functions will include natural-language understanding, real-time speech-to-text, a recommendation engine, memory of prior interactions, and seamless fallback handling. The deliverables will include a conversation flow schema, a fully coded agent integrated with your wine database, web and mobile SDK components, an admin console for customization, test scripts, and a deployment guide. I am eager to discuss further details and initiate the project. Cheers, Vishal Maharaj

@techsty2014

Hello, I've built end-to-end voice agents for hospitality and retail, and your digital sommelier concept is absolutely achievable. The requirement for sub-2s text and sub-3s voice latency, 90% intent accuracy, and conversation memory across 50+ turns is clear—I've delivered similar benchmarks before. Here's my approach: OpenAI GPT-4 for NLU/NLG with custom system prompts trained on wine terminology, paired with ElevenLabs for natural TTS and AssemblyAI for STT (both hit your latency targets). Conversation state stored in Redis so the agent remembers preferences without users repeating themselves. REST hooks pull live catalog data, filter by taste profile, occasion, and pairing context, then rank recommendations using weighted scoring. Web integration via React SDK with voice recording UI, Flutter plugin for mobile with native audio permissions. Admin dashboard lets your team tweak voice persona, fallback messages, and prompt templates without touching code. Deliverables: intent schema, coded agent with API integration, SDK components with sample pages, admin console, test scripts with recorded demos, deployment docs, and live knowledge-transfer session. Questions: • Do you have existing user personas or taste-profile data I can reference? • Should the agent proactively ask about budget constraints or skip that unless mentioned? Thanks and best regards, Faizan

@Joejoe777

Hi there, I have carefully reviewed your project requirements for the AI Wine Expert Voice Assistant and understand the task clearly. The project entails building a conversational 'digital sommelier' that seamlessly combines voice and text chat functionalities to assist users in selecting wines based on their taste preferences, meals, or occasions. The core functions expected include natural-language understanding and generation, real-time speech-to-text and text-to-speech capabilities, a recommendation engine based on preferences and context, memory of prior interactions, and a fallback mechanism with the option for human escalation. The integration environment involves the React web front end and Flutter mobile app, with an adaptable choice of tech stack. The deliverables include a conversation flow schema, a fully coded agent integrating with the wine database, web and mobile SDK components, an admin console for customization, testing scripts, recorded demos, and deployment guidance. Let's discuss in detail through chat.

@rogelitoc

Hello, how are you? I've carefully reviewed the description and I am confident I can deliver it on time. I understand that you need a conversational “digital sommelier” that can seamlessly transition between spoken dialogue and text chat, engaging with users to uncover their taste profiles and suggest wine bottles that suit their preferences. I have hands-on experience in natural language processing, voice technology, and building recommendation engines. Here is my approach as follows: - Develop a comprehensive conversation flow and intent/entity schema to ensure natural interactions. - Implement real-time speech-to-text and text-to-speech functionalities using a suitable TTS/STT service, ensuring a pleasant user experience. - Create a robust recommendation engine that leverages your wine catalog data and user preferences to provide personalized suggestions. I am ready to start immediately and can deliver the result fast. With my past experience in building voice agents, particularly in the food and beverage sector, I understand the nuances that make these interactions feel human. I'd love to discuss in more detail. Best Regards.

@olehd6

Hello, I just came across your project for building a “digital sommelier,” and I’m genuinely excited about it. The idea of creating a seamless conversational assistant that can engage users through both voice and text while providing personalized wine recommendations is a fantastic challenge, one that aligns perfectly with my experience. Here's how I would tackle this: With extensive experience in developing conversational agents, I've successfully implemented natural-language understanding and generation systems using frameworks like Rasa and OpenAI. I can integrate real-time speech-to-text and text-to-speech capabilities utilizing services like Google Cloud or Amazon Polly to ensure your assistant sounds friendly and engaging. My background includes building recommendation engines that pull live data from databases via REST/GraphQL, allowing for dynamic responses based on user preferences. I am well-versed in creating conversation flows and intent schemas tailored to enhance user interaction, ensuring that the assistant remembers context throughout the dialogue. Plus, I can implement fallback handling and escalation processes to maintain a smooth user experience when inquiries go beyond the agent's scope. I'm eager to discuss your goals further and explore how we can make this digital sommelier an invaluable part of your customer experience. Cheers, Oleh