Training AI models, autonomously

Your data gets labeled while you sleep

Tagmatic replaces manual data annotation with AI agents that label, verify, and refine training datasets. Humans review edge cases. Machines handle everything else.

Try the API ↓

Annotations handled by AI

$0B

Market by 2031

Faster than manual labeling

The old way is broken

Every AI company pays humans to click boxes

Data annotation is a $3 billion industry built on manual labor. Thousands of contractors labeling images, rating text, classifying data, one click at a time. It's slow, expensive, and inconsistent.

Turnaround

Days to weeks

Hours

Cost per label

$0.04 - $0.12

Under $0.01

Consistency

Varies by annotator

Uniform quality

Scale

Hire more people

Add more compute

How Tagmatic works

Three steps. No workforce management.

Define your schema

Tell us what labels to apply: sentiment, entities, topics, classifications. Send it as JSON with your data through a single API call.

AI labels it

Claude annotates your data using your schema and guidelines. Every annotation comes with a confidence score. High confidence = auto-approved.

Review edge cases

Low-confidence annotations are flagged for human review. You only look at what actually needs judgment. The rest is production-ready.

Try it now

Live API Playground

Paste any text, define a labeling schema, and watch the annotation engine work in real-time. No signup required.

Request POST /api/annotate

{
  "text": "Apple reported record Q4 revenue of $89.5 billion, beating analyst expectations. CEO Tim Cook attributed the growth to strong iPhone 15 sales in China and India. However, iPad revenue declined 10% year-over-year, raising concerns about the tablet market.",
  "schema": [
    {
      "name": "sentiment",
      "type": "classification",
      "values": ["positive", "negative", "neutral", "mixed"],
      "description": "Overall sentiment of the text"
    },
    {
      "name": "companies",
      "type": "span",
      "description": "Company names mentioned",
      "multi": true
    },
    {
      "name": "financial_figures",
      "type": "span",
      "description": "Monetary values and percentages",
      "multi": true
    },
    {
      "name": "topic",
      "type": "extraction",
      "description": "Primary topic of the text"
    },
    {
      "name": "contains_risk",
      "type": "boolean",
      "description": "Whether the text mentions risks or concerns"
    }
  ],
  "guidelines": "Be precise with spans. For financial figures, include the full number with currency symbol or percentage."
}

Response WAITING

Click "Annotate" to see results here. Each annotation will include: • Label name and value • Confidence score (0.0 - 1.0) • Review status (auto-approved or needs review) • Text spans for entity labels

Developer API

One endpoint. Structured results.

Send text + schema, get back labeled data with confidence scores. That's it. Integrate in 5 minutes.

                    curl
                    Annotate text with a single API call
                

# Annotate text with sentiment + entity extraction
curl -X POST https://tagmatic.polsia.app/api/annotate \
  -H "Content-Type: application/json" \
  -d '{
    "text": "Tesla stock dropped 8% after Elon Musk announced...",
    "schema": [
      {
        "name": "sentiment",
        "type": "classification",
        "values": ["positive", "negative", "neutral", "mixed"],
        "description": "Overall tone"
      },
      {
        "name": "entities",
        "type": "span",
        "description": "Named entities",
        "multi": true
      }
    ],
    "guidelines": "Focus on financial sentiment"
  }'

                    python
                    Python integration example
                

import requests

response = requests.post("https://tagmatic.polsia.app/api/annotate", json={
    "text": "Patient presents with acute chest pain...",
    "schema": [
        {"name": "symptoms", "type": "span", "description": "Medical symptoms", "multi": True},
        {"name": "urgency", "type": "classification", "values": ["low", "medium", "high", "critical"]}
    ]
})

data = response.json()
for ann in data["annotations"]:
    if ann["needs_review"]:
        # Route to human reviewer
        send_to_review_queue(ann)
    else:
        # Auto-approved, use directly
        save_annotation(ann)

Schema Types

Four label types cover every annotation use case.

classification

Choose from predefined categories. Requires values array. Perfect for sentiment, topic, intent detection.

span

Extract exact text with character offsets. Ideal for NER, keyword extraction, entity recognition. Returns start, end, text.

extraction

Free-form text extraction. Summarize, extract topics, pull key information. Returns a string value.

boolean

True/false classification. Does the text contain X? Is it Y? Simple binary decisions with confidence.

The future of annotation

The last data labeling platform you'll ever need

Built by someone who trained AI models by hand and knew there had to be a better way. Tagmatic is annotation without the army.