Standalone LiveKit Voice AI Agent

@shadabkhan92

I'm MOHD SADAB and I just might be the perfect fit for your project. My team's core expertise lies in building AI systems that are built to last and intelligently interact with other components of your ecosystem, just like what you're envisioning. Our experience with agentic AI, LLM integration, and predictive ML models is a great match for the kind of self-hosted, real-time voice assistant on LiveKit you need. Our strength extends beyond our AI skills; our understanding and proficiency in relevant tech stacks is equally robust. We're well-versed in Docker, Python (which can help streamline your speech-to-text and NLP services), and we work seamlessly on Linux-based systems like Ubuntu. Moreover, I've been working on challenging projects like bridging AI with hardware systems (hence enabling AI devices to perform more functions), embedding intelligent agents in enterprise software (thus enhancing every level of productivity) - this demonstrated versatility makes me confident we can deliver precisely what you're seeking. Given your need for independence from third-party providers, I assure you that all aspects of your solution will be internally controlled on premise

@ashinnd84

Hello there, we are a team of Full Stack Web and Mobile App Developers and we can do this project in no time. Thanks Ashish Kumar.

@gsantharam7088

Hi, I’d be happy to assist with your pharmacy data entry project. I understand that accuracy is critical when working with prescription records and inventory logs, especially where drug names, dosages, lot numbers, expiration dates, and stock counts are involved. I pay close attention to detail and always perform a second verification pass to minimize errors. I can efficiently extract data from both selectable PDFs and scanned documents, using OCR where needed and manually reviewing entries to ensure accuracy. The final spreadsheets will strictly follow your provided Excel or Google Sheets template with consistent formatting throughout. You'll receive clean, validated spreadsheets along with a completion report highlighting any unreadable fields or discrepancies encountered during processing. I fully understand the importance of confidentiality and will handle all documents securely with HIPAA awareness. I'm available to start immediately, can complete an initial sample batch for your review, and will maintain the 99%+ accuracy standard throughout the project. I look forward to working with you.

@NEHABHAT92

I'm Neha, an experienced IT professional with over 9+ years of hands-on experience in web and mobile development using Python and other various languages. Your project involves building a standalone voice AI agent with on-premise voice recognition and natural language processing, which aligns perfectly with my skill set. Over the years, I have successfully delivered similar projects where I had to handle audio streams and convert speech to text in real time, while maintaining context and performing NLP. In terms of delivering your expected outcomes, I can generate Docker-compose scripts that will easily spin up the required LiveKit server alongside speech-to-text and NLP services. I'll provide complete source code documentation with a clear README showing how audio frames are captured from LiveKit, transcribed and processed for contextual responses. Additionally, I assure you of a sample conversation that aptly demonstrates our program's ability to handle at least three consecutive customer-support queries while retaining context. Lastly, in alignment with your requirement for open-source models or frameworks, I am conversant with popular open-source models/frameworks which we can employ for this project with complete licensing compatibility. In conclusion, my deep knowledge of Python, extensive IT experience, along with my track record for delivering top-notch solutions make me an ideal fit for your project. Looking forward to discussing more details with you soon!

@srdank001

Hi there, A self hosted voice agent matters because privacy, latency, and full infrastructure control are essential for real time support. I’ve built similar AI pipelines where the key fundamentals were streaming audio handling, local transcription, context retention, and low latency responses. I can implement this with the specialized LiveKit, Docker, Python, Whisper, and local LLM stack for Ubuntu 22.04. Which GPU and RAM are available on your server? Thanks Srdan

@np45

Building a self-hosted voice assistant on LiveKit is right up my alley. Step 1: Set up LiveKit server and integrate a speech-to-text module using Docker Compose on your Ubuntu 22.04. Deliverable: A single command setup with no external API calls. Step 2: Implement a speech-to-text pipeline, capturing and transcribing audio frames via LiveKit. Deliverable: Clear README on capturing, transcribing, and processing audio. Step 3: Develop an NLP layer that maintains conversational context. Deliverable: A demo handling three consecutive support queries accurately. Step 4: Provide performance metrics for latency and context retention. Deliverable: Report on average latency under typical load. To reduce risk, I offer a paid pilot for the initial setup and integration. Let's discuss how to tailor this to your specific requirements. Want to walk through the details before you commit?

@shahid91616

Hi, I’m Md Shahid Ansari. I’ll build a self‑hosted voice‑assistant pipeline that plugs into LiveKit, transcribes audio with Whisper‑cpp, keeps multi‑turn state using a locally fine‑tuned LLaMA/Mistral model (or HuggingFace Transformers) and returns plain text responses. Using Python (and Java where needed), FastAPI and Docker‑compose I’ll spin up LiveKit, the Whisper‑cpp service and the NLP container with a shared Redis store for context. I’ll provide a README, a sample three‑turn conversation, and latency numbers (aim <300 ms). The stack runs on Ubuntu 22.04 with a single docker compose up and makes no external API calls. My recent projects—Finance LLM translator, AI bidding assistant and Instagram automation—demonstrate expertise in Docker, FastAPI, LLM fine‑tuning and real‑time data pipelines, so I can deliver a reliable, on‑prem solution for your support agent.

@JoseRCarvas

The hard part here isn't STT or the LLM on their own, it's holding conversation state across turns while keeping round-trip latency low enough that the caller doesn't feel a gap, all self-hosted with no paid APIs. How I'd wire it: - LiveKit Agents (Python) subscribes to the caller's track and streams frames to STT. - STT: faster-whisper (CTranslate2) locally, with partial transcripts so we start processing before the caller finishes speaking. - NLP/intent + state: a local model (Llama-3-8B or Qwen via Ollama/vLLM) behind an intent layer that keeps per-session context in Redis, so multi-turn support questions resolve correctly. - Reply text goes back to your interface (no TTS this phase, as you said). You'd get a docker-compose that brings up LiveKit plus the STT and NLP services, source with a README showing exactly where frames are captured, transcribed and routed, and a script to test the flow end-to-end from an audio file. All open-source and locally runnable: no Vapi, no ElevenLabs, no cloud subscriptions. I run 5.0 stars on my projects here. Two quick questions: roughly how many intents for the first-line agent, and does your interface expect the reply over a websocket or an HTTP callback? I'll match whatever you've built.

@rishi9343

As an experienced and dedicated full-stack developer with a concentration on mobile solutions, I have the robust understanding your project requires. Boasting over 8 years of involvement in backend service development along with database optimization, I possess the necessary skillset to manage your frontend speech-to-text and NLP demands proficiently.+ My broad proficiency includes Java- a language that will be crucial for your project's core flow in converting speech to text, accurately maintaining contextual comprehension, and formulating intelligent responses at real-time without any dependency on third-party SaaS. Furthermore, my background in Python complements your desired preference for open-source models or frameworks as it is conducive to ensuring licensing compatibility. In addition to my technical prowess, I’m also an effective communicator which would ensure smooth collaboration between us throughout this task. My thorough experience across various platforms including LiveKit's server setup align well with the requirements you've laid out for your deliverables. I am confident that I can deliver precisely what you need - from pristine Docker-compose scripts, to comprehensive source codes, sample conversation demonstrations to performance metrics. I guarantee that the end solution will run seamlessly on your Ubuntu 22.04 box as needed.

@sanarena

Hi, I’m Armin Nikdel. I can build your self-hosted LiveKit voice agent with on-premise STT, NLP, and TTS—no SaaS dependencies. I’ll containerize the full pipeline with Docker so it’s portable and easy to deploy on your infrastructure. The fixed price is INR 12500, and I can deliver a working prototype in around 20 days. For verification, I’ll run a multi-turn test call where the agent correctly identifies intent and maintains context across at least five exchanges. I’ll also measure end-to-end latency from audio-in to audio-out, targeting under 2 seconds on typical hardware. Which open-source STT engine are you leaning toward—Whisper, Coqui, or something else? That choice affects the Docker setup and resource requirements.

@InjectAI

This is the type of AI system I enjoy building. I have experience developing self-hosted AI pipelines involving speech processing, LLMs, RAG, and real-time APIs, with a strong focus on privacy and low latency. For your project, I would build a fully local pipeline integrated with LiveKit, using open-source models such as Whisper/Faster-Whisper for speech recognition and Llama 3, Qwen, or Mistral (depending on your licensing requirements) for contextual conversation. I'll maintain multi-turn conversation state and expose a clean, modular architecture that can be extended easily. Deliverables: Docker Compose setup for LiveKit + STT + NLP services Complete, well-documented source code End-to-end demo with context-aware conversations Performance report including latency measurements README with deployment and configuration instructions The solution will run entirely on your Ubuntu server with no dependency on commercial speech or language APIs. Please check my profile for similar AI, LLM, and automation projects. I will be happy to discuss the architecture and recommend the best open-source stack for your hardware. Best regards

@RobertBehrmannJr

Your requirement for a fully air-gapped LiveKit voice agent — no Vapi, no ElevenLabs, no cloud subscriptions — is exactly the kind of self-hosted pipeline I enjoy building. I've shipped Docker-based LLM and audio-processing stacks before, and this maps cleanly onto that experience. For the stack I'd propose: LiveKit's Python SDK to pull audio frames from the room, Whisper (openai/whisper or faster-whisper with CTranslate2 for lower latency) handling on-premise STT, and Ollama running a quantized Mistral or LLaMA 3 model for the NLP/intent layer with a sliding-window conversation buffer to maintain multi-turn context. Everything orchestrated via docker-compose so your Ubuntu 22.04 box spins it up in one command. All models are MIT or Apache 2.0 licensed, so no licensing friction on your end. Deliverables will include the full docker-compose setup, annotated Python source, a recorded sample conversation covering three consecutive context-dependent support queries, and a latency report (targeting under 3 s end-to-end on CPU; significantly less with a GPU available). My bid is 12000 INR fixed, deliverable in 12 days. One question: what hardware is your Ubuntu 22.04 box running — CPU-only, or does it have a CUDA-capable GPU? That directly determines which Whisper model size and quantization level I'll target to hit acceptable latency.

@saadalxi

Hi, I'm Saadalxi. I bring a versatile skill set combining voice artistry and technical proficiency. I have a clear, adaptable voice suited for narrations, ads, and character work, along with strong computer skills for recording, editing, and delivering polished audio files efficiently. I pay close attention to tone, pacing, and client requirements to ensure the final output matches your vision. I'm available to start immediately and can share voice samples on request. Looking forward to working together on this project!

@stanislavp56

HI, thank you for simple detail, I have built various AI agents, so I know whole structure and workflow well,, I think my experience can be real help for you and can make our communication easier and better. As framworks, I will use LIveKit for real time media handling, Whisper on speech to text Let's make helpful one Thanks Stanislav

@angelc592

Hello, I understand that this project is not just a voice chatbot, but a fully self-hosted AI support agent requiring real-time audio processing, local AI models, and a scalable architecture without depending on commercial APIs. I have experience developing AI-based systems using open-source technologies, including Whisper speech recognition, local language models, and custom Python assistant architectures. My focus is creating reliable, modular, and scalable AI solutions. For this project, I will build a complete pipeline where LiveKit manages real-time audio streaming, Whisper performs local speech-to-text processing, and a contextual NLP layer maintains conversation state and generates intelligent responses using local models. The solution will include: • Docker Compose deployment for Ubuntu 22.04. • Self-hosted speech recognition and NLP services. • Multi-turn conversation context handling. • Clean and maintainable source code. • Technical documentation and setup instructions. • Testing with example customer support conversations. My goal is to deliver a robust AI foundation that provides full control over data, infrastructure, and future scalability while maintaining a professional development approach. I would be happy to discuss your current environment, FAQ data, and technical requirements to design the most suitable architecture for your product.

@libnim3

As a senior Backend and AI Systems Engineer with extensive experience in Docker, Python and other relevant technologies, I am confident in my ability to deliver on your project. I specialize in building scalable systems that perform under real production loads. I prioritize reliability, security, performance, and long-term maintainability - all attributes crucial to your project's success. Given the importance of maintaining conversation context and responding intelligently in real-time, I understand the value of developing robust speech-to-text capabilities and NLP services. Not only can I meet this requirement for you, but my strong skillset across REST, GraphQL and gRPC APIs makes me the perfect fit to seamlessly integrate these components into your existing interface. Additionally, my approach of engineering-first aligns perfectly with your need for controlled behavior, traceable outputs and production-safe architecture. My clients choose me for clear communication, realistic planning and consistent execution - qualities that I believe would greatly complement your project. Choose me as your freelance expert to ensure that your Standalone LiveKit Voice AI Agent project not only performs optimally but also aligns with your broader business goals.

@karanb145

timeline: 12-15 days cost: ₹12,500 Good morning! I can build a fully self-hosted Voice AI pipeline on top of LiveKit using only open-source, locally hosted models. The solution will run entirely on your Ubuntu server without relying on commercial APIs, while preserving multi-turn conversational context and maintaining low latency. Recommended Stack: ✓ LiveKit ✓ Faster-Whisper (STT) ✓ FastAPI + Python ✓ Llama 3/Qwen 3 or Gemma (local LLM via Ollama/vLLM) ✓ Redis for conversation state ✓ Docker Compose Project approach: ✓ Integrate LiveKit audio stream with local STT ✓ Build contextual conversation pipeline with memory ✓ Implement intent handling and FAQ-based responses ✓ Containerize all services for one-command deployment ✓ Benchmark latency, optimize performance, and document the setup What I deliver: ✓ Docker Compose deployment ✓ LiveKit integration ✓ Self-hosted STT & NLP pipeline ✓ Context-aware Voice AI agent ✓ Source code, README & performance metrics ✓ Complete handover Why me? ✓ Experience building self-hosted AI agents and voice applications ✓ Responsive communication with regular progress updates ✓ High-quality, maintainable architecture ✓ Focus on low latency, privacy & scalability ✓ Complete handover with documentation — SK Matrix

@goheerdev

With a deep understanding of Full Stack Development and proven expertise in Python, I am confident that I have the necessary skills to excel in building your standalone LiveKit Voice AI Agent. During my years of experience, I have developed a wide range of applications, many of which exhibit the real-time processing and on-premise functionalities crucial to your project. For instance, I've worked on projects involving speech-to-text algorithms and NLP such as chatbots for different industries. In terms of approach, your demand for open-source or locally runnable models aligns perfectly with my inclination towards licenses like Apache 2.0 or MIT. My familiarity with applicable models such as DeepSpeech and frameworks like TensorFlow and PyTorch will prove invaluable in delivering accurate metrics and ensuring exceptional contextual comprehension throughout the conversations. Notably, I can assure you that my solution will not rely on any third-party SaaS platforms as stipulated. In line with your Ubuntu 22.04 box, all my deliverables will be docker-compose scripts that encapsulate your required LiveKit server, speech-to-text, NLP services etc., paving the way for a simple "docker compose up" command on your end. Sincerely, your project resonates deeply with me

@abdelhafidg

Hi, Your project immediately caught my attention because you're building a fully self-hosted AI voice system rather than relying on third-party AI services. That's exactly the direction many production AI platforms are moving toward. I like your focus on Docker, local models, low latency, and preserving conversation context. Building a modular pipeline from LiveKit to speech recognition, NLP, and contextual responses creates a foundation that can easily evolve into a complete customer support platform without vendor lock-in. Rather than optimizing only for today's MVP, I'd focus on an architecture that's easy to extend with RAG, knowledge bases, multilingual support, and future voice capabilities while keeping deployment simple and maintainable. One question: Is your long-term vision to keep everything fully on-premise, or would you eventually support hybrid deployments for enterprise customers? I'd love to discuss your architecture.