I design scalable, proprietary Voice AI architectures for founders moving away from expensive wrappers like Vapi and Retell.

Building a Voice AI SaaS on open-source frameworks (LiveKit/Pipecat) offers higher margins and better control, but the architectural complexity often scares founders away. Most don't know how to connect SIP trunking, manage WebSocket states, or structure the database for conversational memory—leading them to rely on costly, restrictive wrappers.

I provide the complete architectural design and technical roadmap to build your own Voice AI platform. I break down the complexity into a proven, modular stack that ensures scalability, reliability, and complete code ownership.

🎨 Frontend Architecture: Designing the schema for Analytics dashboards, Agent Configuration UIs, and real-time Call Logs.

⚙️ Backend Logic: Defining the API structure (FastAPI/Python) for critical endpoints like /get-agent-configuration and /save-conversation to manage the agent lifecycle.

🗄️ Data Schema: Structuring the database (PostgreSQL) to store agent personas, granular usage metrics, and conversation transcripts efficiently.

📞 Telephony & SIP: Designing the ingress/egress flow, connecting SIP providers (Telnyx/Twilio) directly to the LiveKit/Pipecat agent.

Ownership: You own the IP, not a third-party API.

Cost: Eliminate the markup on every minute of audio.

Privacy: Keep sensitive conversation data within your own VPC.

Flexibility: Swap LLMs (OpenAI, Anthropic, Llama) or STT providers instantly without vendor lock-in.

Voice AI Frameworks: LiveKit, Pipecat

Languages: TypeScript (Frontend), Python (Backend)

Database: PostgreSQL

Telephony: SIP Trunking (Telnyx, Twilio)