5 AI Integration Patterns Every Business Should Know

The Landscape

AI integration isn't a single thing, it's a spectrum of patterns, each suited to different problems. Here are the five we see making the biggest impact in production systems right now.

1. Retrieval-Augmented Generation (RAG)

The most common pattern we implement. RAG connects a language model to your existing knowledge base, documents, wikis, databases, so it can answer questions grounded in your actual data.

Best for: Customer support, internal knowledge bases, documentation search.

2. Workflow Automation Agents

Autonomous agents that handle multi-step processes. Think: processing incoming emails, routing support tickets, or generating reports from multiple data sources.

Best for: Repetitive multi-step tasks, data processing, operations.

3. Code-Aware Assistants

Tools like OpenClaw that understand your codebase and can assist with development tasks, code review, refactoring suggestions, documentation generation.

Best for: Engineering teams, code quality, developer productivity.

4. Real-Time Classification

Models that categorise incoming data in real-time, whether that's classifying support tickets, detecting anomalies in logs, or tagging content.

Best for: High-volume data streams, content moderation, monitoring.

5. Conversational Interfaces

Custom chat interfaces tailored to your domain. Not generic chatbots, purpose-built conversational systems that understand your business context.

Best for: Customer-facing products, internal tools, onboarding.

Choosing the Right Pattern

The right pattern depends on your specific needs, data, and constraints. Often the best solution combines two or more patterns, for example, a conversational interface backed by RAG.

If you're not sure where to start, we can help. We'll assess your current setup and recommend the approach that delivers the most value with the least friction.