AI Call Center

Voice agent pipeline: speech-to-text → retrieval-grounded answers → text-to-speech → routing.

RoleArchitect and developer

Period2026

Stack

LangChainSpeech-to-textVector searchText-to-speechPython

Data handlingHIPAA · RAG

Grounding corpus contains no PHI; transcripts are handled under the hospital's retention policy.

Problem

Callers waited for humans to answer questions that already lived in policy documents and directories.

Approach

Chained speech-to-text into a retrieval-augmented LLM grounded on approved hospital content, converted the answer back to speech, and routed the call to the right department when the question needed a person.

Architecture

STT → intent + retrieval (vector store over approved docs) → LLM answer with citations → TTS → transfer rules; guardrails keep the model inside the knowledge base.

Caller audio→Speech-to-text→Retrieval (vector store)→LLM answer + citations→Text-to-speech→Route / transfer

By the numbers

STT → RAG → TTSPipeline

LangChainFramework

Rules-based transferHandoff

Outcome

Prototype-to-pilot pipeline that answers routine questions automatically and hands off cleanly to staff.

What I would tell your team

Ask me how this maps onto your EHR, your interface engine and your security review. I can walk through the data flow, the failure modes we hit at go-live, and what I would do differently the second time.

Talk about a similar system

← PreviousNorth Star

Next →Private LLM (air-gapped)