Available patterns
Intent classification and routing
Use a TEXT_UNDERSTANDING node to classify user input and route to specialized handlers. The foundation of any conversational AI app.
Knowledge base RAG
Ground AI responses in your documents using retrieval-augmented generation with Qdrant vector search and the CUSTOM_AGENT node.
Fan-out extraction
Classify documents by type, then route each to a specialized TEXT_EXTRACTION node with tailored prompts and schemas. Scale to dozens of document types.
AI comparison and reconciliation
Compare AI-extracted document data against system-of-record values and generate structured exception reports with match rates and confidence scores.
Hybrid AI + business rules
Combine AI extraction and understanding with deterministic business logic (formulas, eligibility checks, scoring) for auditable decision-making.
Session state management
Manage conversation history and session state across multi-turn interactions using FlowX Database workflows.
How to use patterns
Each pattern page includes:- When to use — the problem it solves and when to reach for it
- Architecture — how the workflow nodes connect
- Implementation — key configuration: prompts, schemas, fork conditions
- Real-world example — where this pattern appears in our tutorials
- Variations — common adaptations
| App type | Common pattern combination |
|---|---|
| Conversational advisor | Intent routing + Knowledge base RAG + Session state + Hybrid AI rules |
| Document processor | Fan-out extraction + AI comparison + Hybrid AI rules |
| Email automation | Fan-out extraction + AI comparison + Session state |
Pattern origins
These patterns are extracted from two production-grade FlowX apps:- Mortgage advisor — a chatbot that evaluates loan eligibility across 7 banks using conversational AI, document extraction, and financial calculations
- Logistics document processor — an email-triggered pipeline that processes 17 document types from carrier emails, reconciles them against a TMS, and surfaces exceptions for review