Customer service chatbots built on large language models have moved from proof of concept to production infrastructure. The difference between a brittle FAQ bot and a reliable resolution agent comes down to architecture, retrieval strategy, and inference economics. This guide covers practical patterns for building LLM-powered support systems that handle multi-turn context, tool use, and long conversation histories, and it explains where Oxlo.ai fits as a backend for these workloads.
Start with a Modular Architecture
A production chatbot is not a single prompt. It is a pipeline. Separate concerns into distinct layers: ingestion for knowledge bases, retrieval for relevant documents, reasoning for response generation, and action for external tool calls. This modularity lets you iterate on the knowledge base without retraining the model, and swap models as requirements change.
For the reasoning layer, you need an inference backend that supports streaming, function calling, and JSON mode out of the box. Oxlo.ai provides fully OpenAI-compatible endpoints for chat, embeddings, and audio, so you can drop its base URL into existing SDKs without rewriting clients.
Select a Model for Your Domain
Not every customer service task requires the largest model. Match capability to complexity.
- General support and triage:
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