CASE STUDY

Hybrid AI Chatbot

A case study on building a chatbot that combines rule-based branching and AI, replacing a monthly SaaS model with in-house operation and handling over 30 inquiries per day.

Overview

Built a chatbot combining rule-based branching and AI. The system was moved from a monthly SaaS model to in-house operation.

Challenge

The project needed a way to classify inquiries, guide users to the appropriate path and support operation within the organization.

Scope

Designed and implemented input classification, conditional routing, AI responses and user guidance, then migrated the system to a self-managed architecture.

Technologies

PythonVercelGoogle SheetsLLM API

Results

Replaced the SaaS setup with a self-managed system centered on a $5 monthly server cost plus API usage. It handled over 30 inquiries per day and recorded an approximately 40% conversion rate.

RELATED SERVICE

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AI Solutions & PoC Development

We prototype RAG-based internal knowledge search and FAQ responses, then test AI-agent automation and no-code or low-code operation where appropriate. Teams can use the prototype to validate business fit before full-scale development.

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