Case study · AI agents & RAG
Korean insurance chatbot & voice assistant
A retrieval-augmented chatbot that answers customer questions from the company’s own insurance documentation in Korean — compared head-to-head with ChatGPT — and a speech pipeline that lets customers ask the same questions by phone.
- 한국어Korean end to endQuestions, source documents and answers all in Korean
- 2channels, one engineChat and phone share the same retrieval and generation
- 7step call flowGreeting, policy check, spoken question, spoken answer
The challenge
General-purpose chatbots answer insurance questions fluently but generically: they describe what insurers usually ask for, not what this company actually requires. Customers needed answers specific to the company’s products and procedures — in Korean, and often over the phone rather than in a chat window.
Our approach
We indexed the company’s insurance documentation and built a RAG chatbot that answers from retrieved passages, then evaluated it against ChatGPT on the same customer questions. For the phone channel, a speech-to-text and text-to-speech pipeline wraps the same engine: the system greets the caller, verifies the policy, takes the question by voice and reads the grounded answer back.
Workflow
How it works, step by step.
The pipeline in 5 stages, from index to respond. Each stage has a clear input, a clear output and a reason to exist.
- 01
Index
Insurance documentation — products, procedures, required documents — chunked and embedded for retrieval.
- 02
Ask
Customers type a question, or speak it on a call; speech-to-text converts it to text.
- 03
Retrieve
The most relevant passages from the company’s documents are pulled for each question.
- 04
Answer
The model writes a Korean answer from those passages — inquiries, policy details and claim-related questions.
- 05
Respond
The answer is shown in chat, or synthesized to speech in real time for the caller.
Results
What it produced.
Same question, two systems
Our RAG chatbot · grounded in company documents
- 신분증 (주민등록증, 운전면허증 등)
- 가족관계등록부 (가족관계증명서)
- 주민등록등본
- 기타 필요한 서류 (예: 사고증명서, 경찰신고서류 등)
The company’s own checklist: ID card, family-relation certificate, resident-registration abstract, and case-specific documents such as accident or police reports.
ChatGPT · general model
- 보험청약서 (회사양식)
- 신분증 (사진이 부착된 정부기관 발행 신분증)
- 기타 회사가 요구하는 증빙서류
A generic answer — an application form, ID, “other documents the company requires” — plus a caveat that requirements vary by insurer.
The RAG answer reflects what this company actually asks for; the general model can only describe what insurers usually ask for.
Voice channel · call flow
- 1System · TTS보험에 가입하셨나요? 네 또는 아니요로 답해주세요.Do you have a policy with us? Please answer yes or no.
- Caller · STT네.Yes.2
- 3System · TTS보험 아이디를 알려주세요.Please tell me your policy ID.
- Caller · STT[Policy ID, spoken]4
- 5System · TTS고객님 질문을 입력해 주세요.Please tell us your question.
- Caller · STT제 보험에 대해 알려주세요.Tell me about my insurance.6
- 7System · RAG → TTS저희는 주택화재보험을 제공하며, 이는 주택 화재로 인한 손해에 대해 보장합니다…Reads back a policy summary generated from the retrieved documents — coverage, how the insured amount is set, how claims are calculated.
Stack & techniques
- Python
- Retrieval-augmented generation
- Vector search
- Korean NLP
- Speech-to-text
- Text-to-speech
Delivered by members of the Saerosense founding team. Client names are withheld unless public — references are available on request.
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