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Overview

KaireonAI lets you create decisioning gates, contact policies, and behavioral metrics by describing them in plain English using the built-in AI chat panel. Instead of manually configuring field names, operators, and values, open the AI assistant and type a sentence like “create a decisioning gate for customers over 25 who spent more than $500 in the last 90 days” — the AI builds structured entity definitions and shows you a preview before creating them. Open the AI chat panel with Cmd+I (macOS) or Ctrl+I (Windows/Linux), or click the AI icon in the top navigation bar.

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How It Works

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Supported Patterns

The AI rule parser understands a wide range of natural language patterns. It uses LLM-powered parsing with a deterministic regex-based fallback if the LLM is unavailable.

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LLM-Powered Parsing

When the LLM is available, the parser uses an AI-SDK structured-object call with schema context to interpret any natural language description. The LLM receives:
  • Your text description
  • Available schemas and their field names
  • Max conditions per rule (configurable, default: 5)
  • Allowed operators (configurable, default: equals, gt, lt, gte, lte, contains, in)

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Heuristic Fallback Patterns

If the LLM call fails, the parser falls back to regex-based heuristics that handle these patterns: The heuristic parser also warns when referenced fields (income, loyalty, score, tier, segment, status, balance) are not found in available schemas, and flags ambiguous terms like “VIP” if no matching field exists.

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Batch Creation

You can describe multiple rules in a single message. The AI parses all entities from the description and shows a preview for each one:
This produces four entities: two decisioning gates, one behavioral metric (with a linked decisioning gate), and one contact policy.

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Schema Context

The rule parser automatically loads your tenant’s Data Schemas and passes the field names to the AI. This means:
  • Field references are validated against your actual data model
  • Warnings are generated for fields that do not exist in any schema
  • The AI can suggest the correct field name if a close match exists

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Configurable Constraints

The rule parser has three built-in constraints. Max Conditions and Allowed Operators honor the per-tenant Rule Building overrides configured in AI Configuration: the parse-rule endpoint loads them from tenant settings and passes them into the parser at request time, falling back to the defaults below when a tenant has not set an override. Field Type Constraints is stored in tenant settings but is not yet referenced by the parser.

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Tips

  • Be specific — Include the field name, comparison, and value. Vague descriptions like “good customers” cannot be parsed into specific conditions.
  • Break complex logic apart — Deeply nested conditions (e.g., “(A AND B) OR (C AND D)”) work better as separate rules combined at the Decision Flow level.
  • Numeric precision — Currency symbols and comma separators are handled automatically. “$1,000” becomes 1000.
  • Time windows create metrics — When you mention “in the last N days”, the parser creates a behavioral metric to track the aggregation, then creates a decisioning gate that references that metric.
The AI chat panel can also be accessed via MCP from external tools like Claude Code and Cursor. See the MCP Server Reference for details.

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Next Steps

Decisioning Gates

Learn how decisioning gates work in Decision Flows.

AI Assistant

Learn more about the built-in AI assistant.

AI Configuration

Configure rule building parameters.