AI Classify

Sends data to an LLM along with a list of labels, and routes execution to the matching label's outgoing branch. If no label matches confidently, the fallback branch fires.

Unlike AI Transform (which returns free-form text), AI Classify forces the LLM to pick one of N predefined outcomes, so downstream routing is deterministic.

When to use

• Risk scoring: classify a customer as low_risk / medium_risk / high_risk.
• Intent detection: is this support ticket a billing, technical, or complaint?
• Sentiment routing: positive, neutral, negative.
• Any "categorize this and branch on the category" use case.

Configuration

Field	Required	What it does
instruction	No	Extra context for the LLM. Keep short — the labels themselves should be self-describing.
inputField	No	The payload to classify. If blank, the upstream node's output is used.
labels	Yes	JSON array of {key, description}. Each becomes an output handle.
provider	No	groq (default), gemini, openrouter.
model	No	Provider-specific model ID.

Labels format

[
  { "key": "low_risk",    "description": "No prior issues, clean history" },
  { "key": "medium_risk", "description": "Some flags but nothing severe" },
  { "key": "high_risk",   "description": "Chargebacks, complaints, or fraud signals" }
]

Each label's key becomes an outgoing handle on the node. The description is what the LLM reads when deciding — so make it specific.

Handles

• One handle per label (e.g. low_risk, medium_risk, high_risk).
• Fallback — fires when the LLM response doesn't match any declared key. Always wire this to something (even just an Emit Event for logging) so you don't silently drop traffic.

What it outputs

{
  label: "medium_risk",
  rawResponse: "medium_risk",
  confidence: null,
  model: "llama-3.3-70b-versatile",
  provider: "groq",
  tokensIn: 128,
  tokensOut: 4,
  durationMs: 640
}

Gotchas

• Label keys must be JSON-safe identifiers. Lowercase with underscores is safest. No spaces, no dashes (they confuse some LLM response parsers).
• Overlapping descriptions: if two labels sound similar, the LLM becomes non-deterministic. Make each description clearly distinct.
• Cost: classification is cheap (low token count), but runs per execution, so high-volume workflows add up. Watch ai_usage_logs to track spend.
• Don't use for branching on exact field values: if you already have a field like customer.tier === 'pro', use Switch or If/Else — they're faster, free, and deterministic. AI Classify is for the cases where the category is implicit in free-form data.