Agentic AI Developer - Voice & Chat Automation

@ZohaibRoy

⭐⭐⭐⭐⭐ Build Intelligent Voice & Chat Automation Solutions for Your Business ❇️ Hi My Friend, I hope you are doing well. I just checked your project requirements and I see you are looking for an Agentic AI Developer. You have no need to look any further, as Zohaib is here to help you! My team has successfully completed 50+ projects in voice and chat automation. I will create intelligent agents that can resolve customer inquiries using natural interactions, ensuring a seamless experience. ➡️ Why Me? I can easily do your voice and chat automation project as I have 4 years of experience in AI development, specializing in voice and chat systems. My expertise includes backend development, API integration, and working with large language models. Additionally, I have a strong grip on cloud platforms and distributed systems, ensuring your project is efficient and scalable. ➡️ Let's have a quick chat to discuss your project in detail and let me show you samples of my previous work. Looking forward to discussing this with you in chat. ➡️ Skills & Experience: ✅ AI Development ✅ Voice Automation ✅ Chatbot Design ✅ Backend Integration ✅ API Development ✅ NLU/NLP Implementation ✅ Cloud Services ✅ Microservices ✅ Performance Optimization ✅ Data Management ✅ System Architecture ✅ User Experience Design Waiting for your response! Best Regards, Zohaib

@corpmember29

Hello, Agentic AI Developer {{{ I HAVE CREATED SIMILAR AI VOICE, CHAT & AUTOMATION SYSTEMS BEFORE AND I CAN SHOW YOU }}} I have carefully reviewed your requirements and can build production-ready Voice and Chat Agentic AI systems that can understand customer intent, reason through multi-step requests, use backend tools and resolve interactions autonomously before escalating to a live agent. I have 11+ years of experience in AI, backend and full-stack development. I can design the agent architecture using LLMs, Python/TypeScript, LangChain or similar orchestration frameworks, RAG, vector databases and secure API integrations. The agents can maintain conversation context, execute tools, retrieve customer information, perform approved actions and intelligently handle failures or handoffs. For voice automation, I can integrate ASR, TTS and real-time voice streaming to create natural conversational experiences instead of traditional IVR menus. For chat, I can build context-aware agents with knowledge grounding, memory, intent handling and enterprise API integrations. I’ll also implement guardrails, authentication, confidence thresholds, fallback/escalation logic, telemetry, logging and evaluation metrics such as containment rate, task completion, latency and failure rate. The architecture will be modular and scalable for AWS, Azure or GCP deployment. I WILL PROVIDE 2 YEARS OF FREE ONGOING SUPPORT AND COMPLETE SOURCE CODE. Thanks, Christina

@nikhilgarg96

Hello Greetings, After reviewing your project description, I feel confident and excited to work on this project for you. But I have some crucial things and queries to clear up. Please leave a message on chat so we can discuss this, and I can share my recent work similar to your requirements. Thanks for your time! I look forward to hearing from you soon. Best Regards.

@toriquldev123

Hello dear, Greetings from Md. Toriqul Islam! We are a dedicated Web Design & Development team with over 10+ years of industry experience, I’m Engineer Toriqul Islam, an experienced Computer Science & Engineering graduate from (RUET). We specialize in building modern, scalable, and user-friendly digital solutions tailored to business needs. What I Offer We help businesses grow online by delivering: • Clean, modern, and responsive website designs • High-performance and scalable web applications • User-focused UI/UX for better engagement and conversion My Technical Expertise We work across a wide range of technologies, including: • Frontend: HTML5, CSS3, Bootstrap, JavaScript, jQuery, Angular, React • Backend: Node.js, PHP, Laravel, .NET, CodeIgniter, Ruby on Rails, Python • CMS & Platforms: WordPress • Database: MySQL, MongoDB • Mobile Development: React Native, Flutter, and more Why Choose me? ✔️ Clean, optimized, and well-documented code ✔️ Reusable and scalable components ✔️ On-time delivery with complete requirement fulfillment We are confident in our ability to turn your ideas into a powerful digital product. Let’s discuss your project and make it a success. Looking forward to working with you! Best Regards, Md. Toriqul Islam

@ShaikhAneesa

Hi there, I understand you need to build production-grade Voice and Chat Agentic AI systems that can reason, use tools, access enterprise data and resolve customer interactions end-to-end before escalating to human agents. I’m confident I can approach this as an agent architecture and automation problem rather than simply wrapping an LLM around an existing IVR. My approach is to first map customer intents, tools, backend actions, knowledge sources and escalation boundaries. Next, I’ll build the agent orchestration layer using Python/TypeScript, LLMs, RAG, APIs and appropriate agent frameworks, with memory, tool calling, authentication and state management. Then, I’ll integrate the voice/chat layer, enterprise REST/GraphQL services and vector knowledge stores, adding retries, guardrails and fallback paths. Finally, I’ll implement telemetry, tracing and evaluation around containment, completion rate, latency and failure modes, then optimize the system through measurable iteration. The focus will be a scalable, low-latency and observable agentic system where autonomy is earned through reliable tool execution and validation, with human escalation whenever confidence or capability is insufficient. Do you already have an existing IVR/chatbot and backend APIs that the agent needs to replace or integrate with, or should the initial architecture be designed from the ground up? I’m ready to start immediately. Warm Regards, Aneesa.

@Hubready

Hello!, I am a Florida-based senior software engineer and this kind of work is exactly where I like to be. Your main challenge is not just “building an AI chatbot” but making voice and chat automation actually useful, reliable, and able to take action through APIs, RAG, and agent flows without becoming brittle. My approach would be: 1. Map the exact user journeys and failure points 2. Build the core agent logic with clean tool/function calling 3. Add RAG over your knowledge base for accurate answers 4. Wire voice and chat channels into one backend flow 5. Test edge cases, latency, handoff logic, and logging before launch I focus on practical systems that feel intelligent, not gimmicky. I pay close attention to requirements because that’s usually where projects either succeed or get messy fast. A few quick questions: 1. What should the agent be able to do beyond answering questions, such as booking, lead capture, support triage, or internal task execution? 2. Do you already have a knowledge base, call flow, or API layer, or should I design that from scratch? 3. Which voice stack are you planning to use, or do you want me to recommend the best option for speed and reliability? Relevant work I’ve done includes AI support assistants for a SaaS dashboard, a voice-driven intake agent for a service business, and an internal RAG chatbot for a logistics team. If you want this done cleanly and with real thought behind it, I’m ready to jump in. -James

@gotucat

Hi, I reviewed your need for agentic Voice and Chat automation that resolves customer interactions end-to-end before handoff to live agents. Your focus on reducing IVR menus and scripted chatbot flows with natural voice/chat experiences is clear. I’ll build LLM-powered AI Voice Agents and agentic pipelines with Natural Language Processing using tool execution, context-aware prompting, and memory/orchestration patterns. I’ll integrate enterprise actions through secure API Development and API Integration, using REST/GraphQL and event-driven microservices, plus RAG with vector databases for grounded answers. I’ll prioritize low-latency, observability, guardrails, and safe escalation so performance and completion rates improve over time. Let’s discuss here now.

@mayankinnovative

The gap between a scripted chatbot and a true agentic system isn't the LLM, it's the moment an agent has to decide whether it's confident enough to execute a real backend action - like updating a customer record or processing a transaction - versus escalating to a human, and getting that threshold wrong in either direction is what makes agentic deployments either unsafe or uselessly cautious. My approach: I'd design the agent's core loop around explicit confidence scoring tied to each tool call, not just the conversational response, so escalation triggers when the agent's certainty about an action - not just its certainty about understanding intent - falls below a defined threshold. For backend integration, I'd build a middleware layer that treats every action (auth, data retrieval, transaction) as a discrete, retryable unit with its own error handling, so a failed API call degrades gracefully into a fallback response rather than the agent hallucinating a completion. RAG grounding I'd implement against a vector store scoped tightly to your knowledge base per intent category, since broad retrieval across all documentation tends to dilute relevance in real-time voice interactions where latency matters. Is there an existing IVR or chatbot system currently in production that this needs to interoperate with during transition, or is this a clean-slate build?

@OutsourceMan

Hello, CnEL India can build an agentic AI solution that combines natural voice/chat interactions with intelligent reasoning, tool execution and secure backend integrations. Our methodology: • Define customer intents, agent capabilities, escalation rules and success metrics. • Design an agent architecture with LLM reasoning, memory, tool calling and orchestration. • Implement RAG using a structured knowledge base and vector search for grounded responses. • Build secure REST/GraphQL integrations allowing agents to retrieve and perform authorised actions. • Develop voice and chat interaction layers with appropriate ASR/TTS and streaming components. • Add guardrails, validation, confidence thresholds and human-agent handoff. • Implement logging, tracing and evaluation metrics for latency, completion and failure analysis. • Optimise caching, inference and API workflows for real-time performance. • Test agent behaviour with realistic scenarios before production deployment. • Build the system modularly so additional channels, tools and enterprise integrations can be added later. We recommend starting with a focused MVP, validating the core agent workflow, then expanding capabilities based on measured results. Best Regards, CnEL India

@ImranAli01

Hello, I’ve read your details and clearly understand that you are looking for production-grade Voice and Chat Agentic AI agents that can autonomously resolve customer intents, execute backend actions, and scale with low latency. This is absolutely doable for me, let's chat and take this forward. My approach is to use Python with LangChain or Semantic Kernel, LLM orchestration, RAG with Pinecone/Redis, and REST/GraphQL microservices to build agents with planning, memory, tool execution, guardrails and intelligent escalation. For voice, I’ll integrate ASR/TTS and streaming while maintaining conversation context. I’ll add telemetry, tracing, evaluation metrics, retries, caching and failure handling so task completion, containment and latency can be measured and improved continuously. As final deliverables you will receive the agent architecture, voice and chat agents, backend integrations, RAG pipeline, vector knowledge layer, safety controls, escalation flows, observability, evaluation framework, scalable deployment and technical documentation. One thing I’d like to confirm before we start: which cloud platform and existing voice/CRM systems should the agents integrate with? Let’s connect to discuss your current architecture and agent roadmap. Best Regards, Imran

@vishal1145

Your agent architecture will fail under concurrent load if you're relying on synchronous LLM calls without implementing proper queue management and streaming responses. This creates 5-10 second wait times that kill voice UX and cause chat abandonment. Quick questions - what's your target p95 latency for voice responses, and are you planning to run multiple agent instances behind a load balancer or use a single orchestrator with worker pools? Here is the architectural approach: - AGENTIC AI: Build multi-step reasoning agents using LangGraph with tool-calling patterns, state persistence in Redis, and fallback logic when confidence scores drop below 0.7 - RAG + VECTOR DATABASES: Implement hybrid search across Pinecone and Azure AI Search with semantic caching to reduce repeated LLM calls by 60-70% - VOICE & CHAT INTEGRATION: Design WebSocket-based streaming for sub-500ms response times using Azure Speech Services with interrupt handling and context carryover I've built similar agentic systems for 2 enterprise contact centers that reduced live agent transfers by 40% while maintaining 92% task completion rates. Let's schedule a 20-minute technical call to walk through your current IVR flow and map the migration strategy.

@mahads31

With nearly a decade of experience in developing AI-driven solutions across various industries, I am Mahad Sheikh, and I believe I can create transformative Agentic AI systems for your voice and chat automation needs. My journey spans from creating scalable applications to architecting reliable infrastructure, reflecting an all-encompassing comprehension of the digital landscape. Throughout my career, there's been one resounding theme: solving complex problems with simplicity and efficiency. This principle is applicable to addressing your objectives as well. My skills in AI Chatbot Development coupled with an extensive Python proficiency give me a strong foundation to build Voice and Chat Agentic Agents that solve customer interactions autonomously, replacing traditional IVR menus and scripted workflows. Moreover, my expertise in API development allows me to adeptly integrate the necessary backend systems. Agility and scalability are crucial for robust automation solutions, which is why I have focused on distributed systems and microservices architecture.

@Microlent

Hello, I am Dr. Rajesh Rolen, PhD in Computer Science & Engineering, with experience of over 20+ years in API Development, AI Chatbot Development, Python As a preferred freelancer in the top 1%, I have done 400+ projects here on freelancer.com, I have 4.9 ratings out of 5 on average, which showcases my quality of work and timely delivery. Key Highlights: - Free Hosting Support on the Cloud or any desired platform. - Free 3 months of post-delivery support to ensure that our client doesn’t face any challenges after the launch of the project. - Free Dedicated tester on projects to ensure quality delivery, so clients don’t need to act as a tester. - 10+ Years experience UI/UX team to ensure intuitive UI. Portfolio: https://www.freelancer.com/u/Microlent Please open the chat and send me a message, so we can have a more detailed discussion about the project to give you the project timeline and cost. Thank you for considering my services. I look forward to engaging in a productive conversation and understanding how I can be of assistance in bringing your project to life. Regards Rajesh Rolen

@abdulhamidh2

Hello I just read your project for developing Agentic AI voice and chat automation, and it’s exactly the kind of challenge I excel at—building intelligent agents that autonomously resolve customer interactions with natural, context-aware conversations. With over 4 years in backend development and deep expertise in Python, LLMs, prompt orchestration, and microservices, I’ve designed scalable agentic architectures integrating REST/GraphQL APIs and RAG pipelines to enhance real-time voice/chat systems. I focus on seamless backend integrations and robust error handling to ensure high completion rates and smooth user experiences. Could you share more about your current system architecture and key pain points you want to address first? Best regards, AbdulHamid

@ianb65

Hi there, Voice and chat automation that still relies on scripted flows will quickly break under real customer intent. I have strong expertise in API Development, AI Voice Agents, and Large Language Models, and I would build your agentic layer to reason across intent, memory, and backend actions. I’d design the orchestration, connect secure REST and GraphQL services, add retrieval grounding, and implement telemetry plus guardrails so completion rates improve safely over time. Best regards, Ian

@mickeysz

Hello, Across this spec, two things decide whether agentic voice and chat actually beats the IVR you are replacing: latency and safe action execution. Let me speak to those. On voice, the experience lives or dies on response time. Below about a second of silence a call feels natural; above it, callers talk over the agent or assume it hung up. A plan-then-tool-then-speak agent easily blows that budget, so the architecture streams throughout: speech recognised as the caller talks, the reply spoken while still being produced, the agent interruptible mid-sentence. That constraint, not the model choice, makes or breaks a voice agent, and it is why I would not let it do open-ended reasoning on the critical path. On action execution, the risk is an autonomous agent performing writes, authentication, updates, transactions, against enterprise systems. A hallucinated read is recoverable; a hallucinated account change is not. So actions go through a validated tool layer with confirmation before any write, idempotency keys so a retry never double-charges, and a hard escalation when confidence drops. Containment only counts if the contained interactions were correct. Grounding is the third piece: the agent answers from RAG over your knowledge base and reads live data through tools, never from model memory, so it cannot invent a policy or a balance. One question: are the backend systems reachable by real-time API, or is Salesforce Agentforce the intended execution layer?

@AneesaIBA

Hello, I have carefully read your project description and understand that you are looking for a skilled professional to design and develop production-ready Voice and Chat Agentic AI systems that can autonomously resolve customer interactions before escalating to live agents. To approach your project I will build context-aware agents around LLM reasoning, tool calling, memory, orchestration, and RAG, connecting them securely with your enterprise systems through REST/GraphQL APIs and middleware services. For voice, I can integrate ASR, TTS, streaming, interruption handling, and low-latency conversational flows, while chat agents will maintain context and dynamically adapt to customer intent. My focus will be on scalability, reliability, observability, and measurable improvements in containment, completion rates, latency, and failure handling. In terms of relevant experience, I have extensive experience developing AI-powered applications, multi-agent workflows, conversational systems, RAG pipelines, API integrations, and automation platforms using Python, TypeScript, LangChain, OpenAI, Claude, vector databases, Node.js, and cloud infrastructure. I’d love to discuss your project further and answer any questions you may have. Feel free to reach out so we can refine the details together. Best Regards, Rabia Shaikh

@Tanmoy236

Hi There! I specialize in Agentic AI, voice and chat automation with 9+ years of experience building scalable LLM agents, RAG pipelines and real-time integrations. Here’s how I can help: 1. Build autonomous voice/chat agents with memory, tools and orchestration. 2. Integrate APIs, RAG, vector databases and enterprise systems securely. 3. Add guardrails, observability, evaluation and scalable architecture. Would you like me to review your current IVR/chat architecture and target integrations?

@starttechno

Building voice and chat agents that resolve customer interactions autonomously—rather than relying on static IVR menus or scripted chatbot flows—requires strong orchestration, reliable tool execution, and measurable safeguards. With 10+ years in software engineering and 249+ delivered projects, I can design this as a production-ready system using Python/FastAPI, an agent orchestration layer such as LangGraph or Semantic Kernel, and REST/GraphQL middleware for secure enterprise actions. I’ll support multi-step reasoning, authentication, data retrieval, updates, retries, confirmations, and escalation when confidence or policy thresholds are not met. For knowledge grounding, I’ll implement RAG with embeddings and a vector store such as Redis or Azure AI Search, including source-aware responses and conversation context. The voice layer can connect streaming ASR/TTS, while chat shares the same agent and tool framework. I’ll also add caching, async processing, structured logs, tracing, latency and containment metrics, prompt/workflow evaluation, guardrails, and fallback handling. The architecture will be containerized for AWS or Azure and designed for real-time concurrency. Do you already have a preferred voice provider and enterprise system APIs available for integration? Muhammad Saad