Beyond the Chat Interface
Most businesses use ChatGPT through the web interface. The real power comes from integrating LLMs directly into your existing workflows via API. Imagine auto-categorizing support tickets, generating invoice descriptions, or summarizing customer calls — all automated within your ERP or CRM.
Integration Architecture
A typical LLM integration follows this pattern:
- Trigger: An event occurs (new email, form submission, database record created).
- Context Assembly: Gather relevant data (customer history, product info, conversation thread).
- LLM Call: Send a structured prompt with context to OpenAI, Anthropic, or a self-hosted model.
- Response Processing: Parse the LLM output and take action (update CRM field, send reply, create task).
Example: Auto-Categorizing Support Tickets
POST https://api.openai.com/v1/chat/completions
{
"model": "gpt-4o",
"messages": [
{"role": "system", "content": "Categorize this support ticket into: billing, technical, feature-request, or other."},
{"role": "user", "content": "My invoice shows wrong GST amount for interstate supply."}
]
}
// Response: { "category": "billing", "confidence": 0.95 }Cost Considerations
GPT-4o-mini costs roughly $0.15 per million input tokens. Categorizing 10,000 support tickets per month costs under $5. The ROI is enormous compared to manual categorization.