DAEMLABS
To deliver accurate, scalable FAQ call support, we built a custom AI voice infrastructure instead of relying on IVRs or black-box platforms.
Customer support teams at high-volume businesses spend the majority of their cost on repetitive, predictable queries — order status, refund timelines, delivery policies. These queries require no human judgment but consume human time at scale. Existing voice bot solutions are either rigid IVR trees with no intelligence, or LLM wrappers with no reliability, no observability, and no production infrastructure around them.
We built a production-ready voice agent platform from the ground up on Pipecat, designed to handle real phone calls and web voice sessions with the latency, reliability, and configurability that enterprise clients need. The platform is transport-agnostic — the same pipeline handles browser WebRTC sessions and phone calls via Twilio without any code duplication. Each client bot is fully configured by a schema-driven config system, meaning the same platform serves multiple clients with completely different voices, personalities, flows, and knowledge bases. A per-turn RAG pipeline using Milvus and a task-specific embedding model ensures the LLM always answers from verified knowledge base content rather than hallucinating. A flow engine drives structured conversation paths while the LLM handles natural language within each step. Every session is fully observable — complete transcripts, per-turn latency breakdowns across STT, LLM, and TTS, and call recordings are captured and stored automatically.
<700ms Voice Latency — End-to-end response time across STT, LLM, and TTS.
Multi-Tenant Architecture — Isolated bots, sessions, and knowledge bases for each client.
Hallucination-Free FAQ Support — RAG-powered answers from verified knowledge sources only.
Full Call Observability — Structured logs, transcripts, latency metrics, and recordings.