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I will advanced ai voice agents, chatbots, db backend with n8n nodejs python vps deploy
BepoSkynet
About this gig
Stop losing leads to slow text-bots and expensive, glitchy AI voice apps.
Most "AI developers" just build basic prompt wrappers that stutter, lag, double-book appointments, and run up thousands in Retell/Vapi markup fees. As an expert AI & Backend Engineer, I build robust production-ready, lightning-fast voice agents and autonomous backend infrastructures designed to scale.
What I Can Build For You:
- Zero-Markup Custom Solutions: Migrating your system from Vapi/Retell to a custom LiveKit/Python server to slash your per-minute costs by up to 70%.
- Advanced Tool Calling: Voice agents that securely check live database inventories, process Stripe payments with strict data deduplication (Idempotency), and book calendars.
- Post-Call Automation Flows: Async workflows that instantly transcribe, run LLM analysis for lead scoring, and push structured JSON summaries directly to your CRM.
- Low-Latency Optimization: Fix sluggish agents to crash through the 1-second latency barrier for fluid, human-like turn-taking.
I Use Vapi, Retell AI, Bland AI, LiveKit (Open Source), Pipecat, Twilio, Telnyx. Python (FastAPI/Node.js), Make.com, n8n, LangGraph, Langfuse, GHL, PostgreSQL,
MESSAGE ME!!
Get to know BepoSkynet
BepoSkynet
AI Voice Agent Conversational Voice Automation Specialist
- FromNigeria
- Member sinceAug 2026
- Avg. response time1 hour
Languages
English, Spanish
I build production-ready AI Voice Agents that talk, listen, reason, take real business action and not scripted bots. Specialties: AI receptionists, inbound/outbound calling, appointment setters, sales & lead qualification, support, call routing, and follow-ups.
I connect your voice agent to phone numbers, CRM, calendars, databases, and workflows using Vapi, Retell AI, ElevenLabs, OpenAI Realtime, Twilio, SIP, GoHighLevel, HubSpot, Salesforce, n8n, Make, custom APIs.
BEPO SKYNET turns conversations into automated business outcomes.
Let's build a voice agent that doesn't just speak, it works.
FAQ
What is the difference between building on Vapi/Retell versus a custom LiveKit backend?
Vapi and Retell are excellent middleware wrappers for fast prototyping, For high-volume or enterprise projects, i build directly using open-source LiveKit and Pipecat. eliminates fees completely, cutting your ongoing operational costs by up to 70% while providing full data privacy and GDPR/HIPAA.
My current voice agent has a 2-second delay. How do you crush conversational latency?
I resolve this by migrating systems to end-to-end speech-to-speech protocols like OpenAI’s Realtime API or Gemini Live, combined with high-speed inference hosts like Groq. This keeps your agent's response latency under 600ms for fluid, human-like turn-taking.
How do you prevent the AI from double-booking appointments or double-charging cards if a call drops?
As a backend expert, I implement Idempotency Keys across all tool-calling actions. Every API request triggered by the voice agent carries a unique cryptographic token tied to that exact conversation_id. Even if the connection drops and the AI attempts to re-run the tool, your backend recognizes it.
Can your voice agents handle customer interruptions ("barge-in")?
Yes,I configure smart buffering and advanced Voice Activity Detection (VAD) parameters within LiveKit/Vapi. The moment the human speaks mid-sentence, the system instantly truncates the agent's outgoing audio stream, clears the active TTS queue shifts focus back to processing the user's new inputs.
Can you integrate these voice agents into custom or legacy CRMs?
Absolutely. I build robust backend API handlers ( Python FastAPI or Node.js) that trigger immediately call ends. The backend captures raw audio transcript, runs an LLM script to extract structural data,converts it into clean JSON, and directly into HubSpot, Salesforce, GoHighLevel (GHL), or any SQL.
What happens if the AI agent encounters an angry customer or a complex issue it can't solve?
Routing protocols, integrating Real-time Sentiment Analysis and Emotion AI modules that track pitch and tone variations (customer's frustration score crosses a specific threshold, or if the user explicitly asks for a manager, the backend triggers immediate SIP Refer / Live Transfer command)
How do you ensure the voice agent actually follows our business script and doesn't hallucinate data?
I avoid bloated, single system prompts which make agents sluggish and prone to making up data architecting a multi-agent state machine using frameworks like LangGraph, Supabase or Pinecone using Retrieval-Augmented Generation (RAG) so it pulls answers only from your approved company knowledge.
My voice calls are connecting, but sometimes there is completely dead silence. Can you fix this?
I specialize in debugging WebRTC and SIP trunk configurations. If you are self-hosting your voice architecture, I will properly configure and deploy ICE/STUN/TURN network servers to ensure audio bypasses strict corporate firewalls every single time.
How do we monitor our calls to see why users are hanging up or where the AI is failing?
I build complete LLM Observability and Tracing pipelines directly into your infrastructure using tools like Langfuse, Helicone, or Arize Phoenix giving a clear dashboard showing where calls drop, tracking partial STT, LLM latency, tool-execution speeds, and full cost breakdowns per call.
