I will self hosted multi agent claude n8n livekit 11labs vapi nodejs python php GPU AWS


About this gig
Building AI voice agents, voice cloning, AI dubbing, or agentic automation but keep running into API limits, high per-character costs, complex tool-calling,unreliable workflows?
I'M here for you, you need a robust self hosted system to solve this .
Full-Stack Custom Dashboard Platforms
- I Build complete Next.js/React dashboards connected to Python FastAPI AI backends, PyTorch models, GPUserver, databases, storage, authentication, and APIs.
Self-Hosted Voice AI Cloning
- I Deploy and integrate open-source models such as F5-TTS, XTTS-v2, and CosyVoice on GPUs such as RunPod or AWS, DigitalOcean, Hetzner, reduces dependence on third-party character and usage limits.
Dubbing And Translation Pipelines
- I Automate pipelines using ElevenLabs Whisper STT, speech diarization, translation, audio alignment, and FFmpeg for multilingual dubbed audio and video.
Custom Middleware & AI Infrastructure
- Use Python, Node.js, or PHP to handle complex APIs, large data payloads, AI processing, and logics outside expensive no-code execution tiers.
LiveKit Vapi ElevenLabs Voice Agents
- Voice agents for RAG receptionist, customer support, qualification, appointment booking, and outbound calls.
Message me
Get to know Bepo V
FULL STACK AI VOICE PRODUCTION ENGINEER
- FromUnited Kingdom
- Member sinceJul 2025
- Avg. response time1 hour
- Last delivery1 year
Languages
English
My Portfolio
FAQ
Can you build a voice cloning platform with no third-party character limits?
Yes. A self-hosted architecture can reduce dependence on third-party per-character or usage-based limits. Your actual capacity will depend on the selected model, GPU resources, concurrency, audio quality, and infrastructure configuration.
Can you build an AI dubbing system for videos?
Yes. I can build an automated AI dubbing pipeline that processes audio/video, transcribes speech, detects speakers, translates the content, generates cloned voices, synchronizes the audio, and produces the final dubbed media.
What technology do you use for AI dubbing?
Depending on the project, I can combine Whisper for speech-to-text, speaker diarization, translation models/APIs, F5-TTS, XTTS-v2, CosyVoice or ElevenLabs for voice synthesis, and FFmpeg for audio/video processing and alignment.
Can you build both the frontend and AI backend?
Yes. I can handle the complete full-stack implementation, including a Next.js/React dashboard, Python FastAPI backend, AI processing engine, database, authentication, file storage, GPU workers, APIs, and deployment infrastructure.
Do you write custom code inside n8n, Make, or Zapier?
Absolutely. While I use these platforms for visual orchestration, I write custom JavaScript/Python inside Code Nodes—or build standalone PHP/Python microservices—to handle heavy data parsing, pagination, and multi-tier API authentication efficiently.
Can you help me migrate from Zapier/Make to a self-hosted n8n instance?
Yes. I specialize in deploying self-hosted n8n using Docker on your cloud infrastructure (AWS, DigitalOcean, Hetzner). I will migrate your workflows, configure task runners, and set up security protocols to ensure your data processing aligns with regional privacy guidelines.
How do you prevent LiveKit agents from breaking when a user interrupts them?
I write specialized Python or Node.js state-management middleware. If a user interrupts the bot mid-sentence, the middleware intercepts the event, safely pauses or rolls back the active background tool-call, and updates the agent's context window instantly to prevent database corruption.
Can you build multilingual AI voice and dubbing systems?
Yes. I can build multilingual pipelines that combine speech recognition, translation, speaker handling, voice cloning/TTS, and audio alignment. Supported languages and voice quality depend on the models and services selected for your project.
Can you build a proof of concept before developing the complete platform?
Yes. For complex voice cloning or dubbing platforms, I recommend starting with a focused proof of concept. This allows us to validate voice quality, language support, speaker handling, processing speed, synchronization, and GPU requirements before investing in the complete production system

