--- title: "Hire Senior LiveKit Developers | Voice AI & Real-Time Communication | Vindler" description: "Hire senior LiveKit engineers for production voice AI agents, SIP integration, and real-time communication systems. We build with Deepgram, Cartesia, Twilio, and Rime for sub-500ms voice pipelines." url: "https://vindler.solutions/hire/livekit-developers" locale: "en" updatedAt: "2026-11-12" --- # Production Voice AI Agents Built with LiveKit Sub-500ms voice response times. SIP telephony integration. Real-time WebRTC streaming. We build voice AI systems with LiveKit, Deepgram, Cartesia, and Twilio that work beyond the demo, from the first call to the millionth. Tell Us About Your Project Built With [AWS Partner Network](https://aws.amazon.com/partners/)[LiveKit](https://livekit.io/) Recognized by Clutch [Top Clutch AI Consulting Company Brazil 2026](https://clutch.co/profile/vindler-solutions)[Top Clutch AI Company Brazil 2026](https://clutch.co/profile/vindler-solutions)[Top Clutch AI Company São Paulo 2026](https://clutch.co/profile/vindler-solutions)[Top Clutch Chatbot Company Brazil 2026](https://clutch.co/profile/vindler-solutions) ## What We Build with LiveKit From voice AI prototypes to production telephony systems handling thousands of concurrent calls. ### Production Voice AI Agents End-to-end voice pipelines using LiveKit Agents with Deepgram STT and Cartesia or Rime TTS. We build agents that listen, think, and respond in under 500 milliseconds, handling interruptions, turn-taking, and natural conversation flow that users expect from production voice systems. ### SIP Trunking & Telephony Integration Connect your AI agents to the phone network through LiveKit SIP and Twilio. We handle inbound and outbound calling, IVR replacement, call transfer to human agents, DTMF tone detection, and the telephony edge cases that only surface when real customers start calling. ### Real-Time WebRTC Communication Low-latency audio and video streaming built on LiveKit's WebRTC infrastructure. We implement browser-based voice interfaces, multi-party conferencing with AI participants, and real-time transcription overlays, all optimized for the latency constraints of live conversation. ### Multimodal Voice Interfaces Voice-first applications that combine speech with touch, text, and visual elements. We build accessible interfaces for iPads, kiosks, and web browsers where users can speak, tap, or type, and the AI responds through the most appropriate channel. ### Voice Pipeline Observability Full-stack monitoring of your voice AI system with LangFuse and Arize. We instrument every stage of the pipeline (STT latency, LLM inference time, TTS generation, WebRTC delivery) so you can identify bottlenecks, track costs per conversation, and catch quality regressions before users notice. ### Edge & Cloud Voice Deployment Deploy voice agents on cloud infrastructure for scale or on edge devices like Raspberry Pi and Mac Mini for low-latency local processing. We architect hybrid deployments where STT and TTS run at the edge while LLM inference happens in the cloud, optimizing for both cost and response time. Built by Senior Engineers ## Why Voice AI Needs Senior Engineers, Not Tutorial Followers A LiveKit voice agent demo takes an afternoon to build. A production voice system takes months of engineering decisions that determine whether your users have a conversation or a frustrating experience. The difference is in the details: how the system handles overlapping speech, what happens when the STT provider returns garbage during a noisy call, how the agent recovers when the LLM takes 3 seconds instead of 300 milliseconds, and how you debug a conversation that went wrong at 2 AM. Voice AI has unique failure modes that text-based systems never encounter. Echo cancellation that works in a quiet office fails in a car with the windows down. Endpointing algorithms that detect when a user has stopped speaking need tuning per use case, because a 1-second pause means something different in a medical consultation than in a quick customer service call. SIP integration with telephony providers involves protocol-level debugging that most developers have never touched. We shipped a production LiveKit voice assistant for Medicare patients in two weeks, from first line of code to real users on iPads. We actively contribute to the LiveKit open source community, file issues, and help other developers in the forums. When you hit a wall with LiveKit, we have likely already solved it, or we know the maintainers who can help. ## Our Voice AI Tech Stack The full stack for building, deploying, and monitoring production voice AI systems. LiveKit LiveKit Agents LiveKit SIP Deepgram Cartesia Rime ElevenLabs Twilio WebRTC Python Next.js FastAPI OpenAI Anthropic Claude AWS Bedrock LangFuse Arize Sentry ## Voice AI Systems We Have Deployed Production voice systems delivering measurable results for real users. [MVP deployed in 2 weeks ### Voice AI for Medicare Patients Built a LiveKit Agents voice assistant for Medicare patients on iPads. Deepgram STT and Cartesia TTS deliver sub-500ms response times. Multimodal interface combines voice with touch for accessibility, with full pipeline observability through LangFuse. Read Case Study](https://vindler.solutions/case-studies/medicare-voice-assistant)[Replaced legacy IVR system ### AI-Powered Product Recommendation IVR Replaced a rigid IVR menu tree with an AI-powered conversational system. Customers speak naturally instead of pressing buttons, and the system routes to the right product recommendations using real-time inventory data. Read Case Study](https://vindler.solutions/case-studies/product-recommendation-ivr)[75% resolved without human intervention ### Voice-Enabled Customer Support Extended a text chatbot into a full voice channel using LiveKit. Customers call in, speak to an AI agent that handles 75% of requests autonomously, and get seamlessly transferred to a human agent with full conversation context when needed. Read Case Study](https://vindler.solutions/case-studies/llm-customer-chatbot) ## How We Work From voice pipeline design to production deployment in weeks, not months. Step 1 ### Discovery Call A 30-minute technical conversation about your voice AI use case. We discuss your telephony requirements, latency targets, expected call volumes, and integration points. We map out the voice pipeline architecture that fits your constraints. Step 2 ### Architecture Proposal Within a week, we deliver a detailed proposal covering STT/TTS provider selection, LiveKit deployment topology, SIP integration design, and latency budget breakdown. You get a clear picture of how every millisecond is spent in the voice pipeline. Step 3 ### Build & Ship We ship a working voice agent in the first two weeks, not a slide deck. Iterative development with weekly demos, progressive addition of telephony features, and full observability from day one so you can monitor every conversation in production. ## Frequently Asked Questions ### ### ### ### ### ### ### ### ## Ready to Build Production Voice AI? Tell us about your voice AI project and we will respond within 24 hours with an initial assessment. Whether you need a new voice agent, SIP telephony integration, or help scaling an existing system. Free 30-minute discovery call Voice pipeline architecture proposal within one week Working voice agent in the first two weeks ### Get a Free Assessment Describe your voice AI project and we'll assess how LiveKit can power your production voice system. ## Structured data ```json {"@context":"https://schema.org","@type":"Organization","@id":"https://vindler.solutions/#organization","name":"Vindler Solutions","alternateName":"Vindler","legalName":"Vindler Desenvolvimento de Sistemas de IA Ltda - EPP","url":"https://vindler.solutions","logo":"https://vindler.solutions/logo.png","description":"Vindler is a global AI engineering consultancy specializing in custom AI solutions, LLM integration, and enterprise-grade AI systems.","foundingDate":"2025-06-02","foundingLocation":{"@type":"Place","name":"São Paulo, Brazil"},"taxID":"61.102.920/0001-05","email":"contact@vindler.solutions","telephone":"+1-646-846-5339","numberOfEmployees":{"@type":"QuantitativeValue","minValue":1,"maxValue":10},"areaServed":[{"@type":"Country","name":"United States"},{"@type":"Place","name":"Europe"},{"@type":"Country","name":"Brazil"}],"knowsAbout":["Multi-agent systems","Retrieval-augmented generation","Voice AI agents","LangChain","LangGraph","LiveKit","AWS Bedrock","Model Context Protocol","LLM observability and evaluation"],"founder":{"@type":"Person","@id":"https://vindler.solutions/about#carlos-dutra","name":"Carlos Dutra","jobTitle":"Founder and CEO","url":"https://vindler.solutions/about","sameAs":["https://www.linkedin.com/in/carlosdutra","https://github.com/cdutr"]},"contactPoint":{"@type":"ContactPoint","contactType":"Customer Support","email":"contact@vindler.solutions","telephone":"+1-646-846-5339","url":"https://vindler.solutions/contact","availableLanguage":["English","Portuguese","Spanish"]},"address":[{"@type":"PostalAddress","addressLocality":"São Paulo","addressRegion":"SP","addressCountry":"BR","streetAddress":"Av. 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It provides the infrastructure for low-latency audio and video streaming, plus a purpose-built Agents framework for voice AI applications. We use LiveKit because it handles the hard parts of real-time communication (NAT traversal, codec negotiation, adaptive bitrate, echo cancellation) so we can focus on building the AI logic. Unlike proprietary platforms, LiveKit gives us full control over the deployment, from self-hosted on-premise installations to managed cloud deployments."}},{"@type":"Question","name":"How do you achieve sub-500ms voice response times?","acceptedAnswer":{"@type":"Answer","text":"Voice latency is the sum of five stages: audio capture, speech-to-text, LLM inference, text-to-speech, and audio delivery. We optimize each one. For STT, we use Deepgram's streaming API which returns partial transcripts as the user speaks, so we can start processing before they finish talking. For LLM inference, we use streaming responses and start TTS generation on the first sentence rather than waiting for the complete response. For TTS, Cartesia and Rime both offer low-latency streaming synthesis. We also co-locate services to minimize network hops and use LiveKit's built-in audio processing to reduce capture latency."}},{"@type":"Question","name":"How does LiveKit SIP integration work with Twilio?","acceptedAnswer":{"@type":"Answer","text":"LiveKit SIP allows your voice AI agents to connect to the public telephone network. Twilio provides the SIP trunking, giving you phone numbers and PSTN connectivity. When a customer calls your Twilio number, the call is routed via SIP to your LiveKit server, where an AI agent picks up and handles the conversation in real time. We configure the SIP endpoints, handle authentication, manage call routing rules, and implement features like call transfer, hold, and DTMF detection. This means your voice AI agent is reachable from any phone, not just through a web browser."}},{"@type":"Question","name":"What is the difference between Deepgram, Cartesia, Rime, and ElevenLabs?","acceptedAnswer":{"@type":"Answer","text":"Deepgram is a speech-to-text provider known for fast, accurate transcription with excellent streaming support. Cartesia is a text-to-speech provider optimized for low latency and natural-sounding voices, making it ideal for real-time conversation. Rime focuses on expressive, high-quality TTS with fast generation times. ElevenLabs offers the most natural-sounding voices with extensive voice cloning capabilities, but at higher latency. We typically pair Deepgram for STT with Cartesia or Rime for TTS in latency-sensitive applications, and use ElevenLabs when voice quality and brand voice consistency matter more than raw speed."}},{"@type":"Question","name":"Can you replace our existing IVR system with a voice AI agent?","acceptedAnswer":{"@type":"Answer","text":"Yes, and this is one of the most common use cases we implement. Traditional IVR systems force callers through rigid menu trees ('Press 1 for sales, press 2 for support'). A LiveKit voice AI agent lets callers speak naturally and routes them intelligently based on intent. We integrate with your existing telephony through SIP trunking, so your phone numbers and call routing infrastructure stay the same. The migration can be gradual: start by handling one call type with AI and fall back to the existing IVR for everything else, then expand as you validate the results."}},{"@type":"Question","name":"How do you handle voice AI in noisy environments?","acceptedAnswer":{"@type":"Answer","text":"LiveKit includes built-in noise suppression and echo cancellation through Krisp integration. On top of that, we configure Deepgram's endpointing settings to handle environments where background noise might cause false triggers. We implement voice activity detection (VAD) to distinguish between speech and ambient noise, and we tune the interruption detection so the agent does not cut off users in loud settings. For deployment on physical devices like kiosks or tablets, we also test with realistic noise profiles and adjust the audio processing pipeline accordingly."}},{"@type":"Question","name":"What observability do you implement for voice AI pipelines?","acceptedAnswer":{"@type":"Answer","text":"We instrument every stage of the voice pipeline with LangFuse for trace-level observability. Each conversation generates a trace showing: STT latency and confidence scores, LLM prompt and response with token counts, TTS generation time, end-to-end response latency (time from user stops speaking to agent starts speaking), and conversation quality metrics. We also integrate Sentry for error tracking and alerting. This gives you dashboards showing cost per conversation, average response latency, STT accuracy rates, and conversation completion rates, so you can optimize both the user experience and the infrastructure costs."}},{"@type":"Question","name":"Can LiveKit voice agents work on mobile devices and iPads?","acceptedAnswer":{"@type":"Answer","text":"Yes. LiveKit's WebRTC-based architecture works natively in mobile browsers and can be embedded in native iOS and Android apps through LiveKit's client SDKs. We have built voice AI interfaces for iPads specifically designed for accessibility, combining large touch targets with voice interaction for users who may not be comfortable with small text interfaces. The same LiveKit server handles both web and mobile clients, so your voice agent works consistently across all devices without maintaining separate codebases."}}]} ```