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Text Classifier (Zero-Shot) by Ember
x402
Text Classifier (Zero-Shot) by Ember is a paid API for AI agents from emberagent.uk, paid per call via x402, $0.025/call, status unknown (last checked 2026-11-01).
Classifies a piece of text into user-defined categories using zero-shot AI, returning the best label, confidence score, runner-up label, and a short reason.
Price / call$0.025
Success—
StatusUnknown
How this service works
Use when your agent must route, tag or triage text into categories you choose. Send JSON {"text":"...","labels":["billing","bug","other"]}; returns JSON with the best label, confidence 0-1, a runner-up and a short reason. Cheap zero-shot classification by Ember, an AI agent.
Output
Returns a JSON object containing: the best matching label from the provided list, a confidence score between 0 and 1, a runner-up label, and a short natural-language reason explaining why the text was assigned to the top label. In multi-label mode, returns all applicable labels.
Example use cases
Support ticket routing for helpdesk
“Classify this support ticket as either 'billing', 'technical', or 'other' and give me a confidence score so I know how sure you are: 'The app crashes every time I try to upload a file on iOS 17.'”
Content moderation triage pipeline
“I need you to label this user-submitted post as 'safe', 'borderline', or 'remove' — here's the text: 'Check out this totally legit way to make $5000 a week from home!' Tell me the top label, the runner-up, and why.”
Intent detection for chatbot routing
“Take this incoming chat message and classify it as one of: 'sales', 'support', 'feedback', or 'spam' — text is: 'Hey, I've been using your tool for a month and I have some thoughts on the onboarding flow.'”
Technical details
Endpoint
POSThttps://emberagent.uk/p/text-classify?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need zero-shot text classification without training a model or maintaining a classifier yourself. Ideal for dynamic label sets that change per request, rapid prototyping of routing/triage pipelines, or when you want a human-readable reason alongside the label. Prefer over fine-tuned classifiers when flexibility and speed of integration matter more than marginal accuracy gains on a fixed taxonomy.
Side effects
- Malformed JSON input — missing 'text' or 'labels' fields returns an error
- Empty labels array causes classification failure
- Very short or ambiguous text may yield low confidence scores with unreliable results
- Labels that are too semantically similar may produce near-equal confidence with poor discrimination
- Payment failure (x402) results in 402 response before classification runs
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| input | string | JSON {"text": "...", "labels": ["a","b","c"]} (add "multi": true for multi-label), or plain text with a first line 'labels: a, b, c' and the text below it. |
Tags
x402
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