You will get a real-time AI voice agent with sub-500ms response latency
Rising Talent
Project details
Voice is the most natural interface for AI. But most "voice bots" feel robotic: 3-second delays, awkward pauses, and they talk over the user.
I am a Senior AI Engineer specializing in real-time, multimodal AI systems. I build full-duplex voice agents that listen, reason, and speak back in under 500 milliseconds, and gracefully handle interruptions like a human would.
What I Will Do For You:
Design: Map your exact latency budget across VAD, STT, LLM inference, and TTS.
Build: Implement a WebSocket streaming pipeline with Silero VAD (precise endpointing) and Faster-Whisper (low-latency transcription).
Reason: Connect the LLM via LangGraph with validated tool calls so the agent can query your CRM, database, or booking system.
Speak: Wire streaming TTS so the agent starts talking before the full response is generated, with barge-in handling when the user interrupts.
My Tech Stack:
Audio: Silero VAD, Faster-Whisper / Deepgram, Kokoro / ElevenLabs
Reasoning: Local or API LLM
Transport: FastAPI WebSockets / WebRTC
Stop shipping robotic voice bots. Ship a voice agent that feels human. Select the package that fits your needs, or message me to discuss a custom scope
I am a Senior AI Engineer specializing in real-time, multimodal AI systems. I build full-duplex voice agents that listen, reason, and speak back in under 500 milliseconds, and gracefully handle interruptions like a human would.
What I Will Do For You:
Design: Map your exact latency budget across VAD, STT, LLM inference, and TTS.
Build: Implement a WebSocket streaming pipeline with Silero VAD (precise endpointing) and Faster-Whisper (low-latency transcription).
Reason: Connect the LLM via LangGraph with validated tool calls so the agent can query your CRM, database, or booking system.
Speak: Wire streaming TTS so the agent starts talking before the full response is generated, with barge-in handling when the user interrupts.
My Tech Stack:
Audio: Silero VAD, Faster-Whisper / Deepgram, Kokoro / ElevenLabs
Reasoning: Local or API LLM
Transport: FastAPI WebSockets / WebRTC
Stop shipping robotic voice bots. Ship a voice agent that feels human. Select the package that fits your needs, or message me to discuss a custom scope
AI Algorithms
Autoencoder, Convolutional Neural Network, Deep Belief Network, Generative Adversarial Network, Large Language Model, Multimodal Large Language Model, Self-Organizing Map, Transformer Model, Variational Autoencoder, YOLOAI Applications
AI Chatbot, AI Mobile App Development, AI Text-to-Speech, AI-Generated Code, AI-Generated Music, AI-Generated Video, AIOps, Automatic Speech Recognition, Conversational AI, Natural Language Generation, Sequence Modeling, Speech SynthesisAI Development Language
PythonAI Tools
Azure OpenAI, Copy.ai, GitHub Copilot, Gradio, Hugging Face, NVIDIA AI Platform, PyTorch, Streamlit, TensorFlow, Word2vecAI Models
AlphaCode, ChatGPT, DALL-E, Dolly, GPT-4, GPT-J, GPT-Neo, Jurassic-2, LLaMA, OpenAI Codex, Stable Diffusion, WhisperWhat's included
| Service Tiers |
Starter
$49
|
Standard
$599
|
Advanced
$1,400
|
|---|---|---|---|
| Delivery Time | 1 day | 5 days | 12 days |
Number of Revisions | 2 | 3 | 5 |
AI Model Integration | - | ||
Batch Normalization | - | - | - |
Database Integration | - | ||
Detailed Code Comments | - | ||
Image Upscaling | - | - | - |
MLOps | - | ||
Model Deployment | - | - | |
Model Documentation | - | - | - |
Model Monitoring | - | - | - |
Model Testing & Optimization | - | - | - |
Model Tuning | - | - | - |
Natural Language Processing | - | ||
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | - | ||
Setup File | - | - | - |
Source Code | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$350 - $600
Additional Revision
+$250Frequently asked questions
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MM
Misch M.
Aug 26, 2026
Freelancers Wanted to Test an Accounting App
Conz was an absolute pleasure to work with. He was incredibly thorough and detailed in his work, and it’s clear that he takes genuine pride in his craft. I would highly recommend his services to anyone looking for someone who is dedicated and takes their work seriously. His detailed opinion is worth it's weight in gold
About Conz
AI Engineer | Production RAG, Agentic Workflows & LLM Optimization
Buchs, Switzerland - 10:51 pm local time
I’m an AI engineer and my entire focus is on getting LLMs, RAG pipelines, and autonomous agents into production, keeping them fast, and making sure they don’t hallucinate. I don’t just chain together basic API calls; I build systems that handle errors, respect data privacy, and actually solve business problems.
Here is how I approach the core pillars of modern AI development:
1. Agentic Workflows (The Brain)
I use frameworks like LangGraph to build agents that can actually reason and recover from mistakes. Instead of a linear script that crashes on a bad API response, I build cyclic workflows where the agent can evaluate its own output, retry a tool call, or escalate to a human if confidence is low.
2. Enterprise RAG (The Memory)
Basic vector search isn't enough anymore. If you want accurate answers, you need hybrid retrieval. I build pipelines that combine dense vector search with keyword (BM25) search, followed by a cross-encoder reranker. This ensures the LLM actually gets the right context before it tries to generate an answer, drastically cutting down hallucinations.
3. Document Intelligence & OCR (The Eyes)
You can't build a good RAG system if your data ingestion is broken. I build robust Intelligent Document Processing (IDP) pipelines to handle messy, real-world data. Whether it's extracting complex tables from scanned PDFs, processing invoices, or handling multi-modal inputs, I combine advanced OCR (PaddleOCR, Tesseract) with Vision LLMs to turn unstructured documents into clean, structured data before it ever hits your database.
4. Real-Time Voice Agents (The Voice)
Text isn't always the best interface. I build low-latency, full-duplex voice agents that feel natural and responsive. This requires a tightly optimized pipeline: precise Voice Activity Detection (VAD) to prevent the AI from talking over the user, ultra-fast Speech-to-Text (STT/ASR) for accurate transcription, and streaming Text-to-Speech (TTS) to achieve sub-500ms Time-to-First-Byte. The result is a voice agent that can query your RAG system or execute tools in real-time, without awkward robotic delays.
5. Optimization & Infrastructure (The Engine)
High API bills and slow latency kill AI products. I specialize in deploying open-source models efficiently. Whether that means using vLLM for high-throughput continuous batching, applying 4-bit quantization (AWQ/GGUF) to run on cheaper hardware, or fine-tuning a smaller model to replace a massive, expensive one, I focus on making your infrastructure cost-effective and fast.
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💻 Tech Stack & Capabilities
Agentic Frameworks:
LangGraph, CrewAI, LangChain, LlamaIndex, Multi-Agent Orchestration, HITL Approval Gates
RAG & Document Intelligence (OCR):
Hybrid Search (Vector + BM25), Cross-Encoder Reranking, Qdrant, Milvus, pgvector, LlamaParse, Unstructured, PaddleOCR, Tesseract, Vision LLMs
Voice & Audio Processing:
Real-Time Voice Agents, ASR/STT (Faster-Whisper, Deepgram), Streaming TTS (ElevenLabs, Kokoro), Voice Activity Detection (Silero VAD), WebSockets/WebRTC
LLM Optimization & Fine-Tuning:
vLLM, TensorRT-LLM, Ollama, Quantization (GGUF / AWQ / GPTQ), PagedAttention, Continuous Batching, LoRA/QLoRA, Knowledge Distillation
Backend & Production Engineering:
Python, FastAPI, Docker, Kubernetes, PostgreSQL, Redis (Semantic Caching), Prometheus, Grafana, RBAC, GDPR/HIPAA Compliance
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How I Work:
I write code the way professional software should be written: modular, well-documented, and ready for the next engineer to pick up. I care about your API bills, your latency metrics, and whether the system is actually maintainable six months from now. I also strictly adhere to NDAs and data compliance standards.
If you’re tired of proof-of-concepts and need something that actually scales, let’s talk. Send me a message with a brief description of what you’re trying to build and where it’s currently bottlenecked (e.g., accuracy, latency, cost, or data ingestion). I’ll give you a straight, no-fluff assessment of how we can fix it.
Steps for completing your project
After purchasing the project, send requirements so Conz can start the project.
Delivery time starts when Conz receives requirements from you.
Conz works on your project following the steps below.
Revisions may occur after the delivery date.
Kickoff & Use-Case Scoping
We define the agent's role, voice persona, and which backend systems (CRM/DB) it needs to query.
Latency Budget & Stack Design
I map the millisecond budget across VAD, STT, LLM, and TTS, and select the optimal stack for your budget.