AI Data Labeling in India

Generate Training Labels with AI

Label datasets for machine learning—text classification, object detection, entity extraction—without manual crowdsourcing. Use AI to generate candidate labels; humans review; train your models.

Why AI labeling beats crowdsourcing

Speed: Label 100k samples in hours, not weeks. Cost: ₹ per token, not per-label SaaS subscription. Consistency: One labeling prompt ensures uniform quality. Scalability: No crowdworker availability bottleneck. Auditability: Every label traced to the model + prompt that created it.

Models for data labeling

Model	Input/Output (₹)	Reasoning	Best for
Qwen3.8-27B	₹16.32 / ₹48.96	Good	High-volume, budget labeling, vision-capable
Claude Sonnet	₹201.6 / ₹1008	Excellent	Nuanced categories, edge cases
Claude Opus	₹504 / ₹2520	Premium	Highest-stakes labeling (medical, legal)
GPT-5	₹126 / ₹1008	Advanced reasoning	Complex multi-label tasks

Generate labels in 3 steps

Step 1: Define your labeling rules

Label each sample as one of: [Spam, Ham, Suspicious]
Rules:
- Spam = marketing, unsolicited ads
- Suspicious = unusual patterns, risky indicators
- Ham = legitimate, normal

Respond with ONLY the label and confidence (0.0–1.0).

Step 2: Batch-call the API

POST https://api.unoblox.ai/v1/chat/completions
Authorization: Bearer ub-gw-...

{
  "model": "qwen/qwen3.8-27b",
  "messages": [
    {"role": "user", "content": "[Your rules]\n\nSample: [Your text]\nLabel:"}
  ],
  "max_tokens": 50
}

Step 3: Review and store — Filter labels by confidence; human-review low-confidence samples; store final labels in your training DB.

Real scenario: Label 50k images for product classification

1. Send each image directly to Qwen3.8-27B (vision-capable) with your category list — no separate description step needed.
2. Parse the returned label and confidence score.
3. Humans review all low-confidence predictions (~5% of 50k).
4. Store verified labels; train your classifier.
5. Cost: ~15M input + 1M output tokens ≈ ₹294 total on Qwen3.8-27B; human review = 5–10 hours.

Quality control workflow

1. Auto-label: Generate labels with AI.
2. Confidence filtering: Keep only >0.95 confidence.
3. Spot-check: Humans verify 1–5% of confident labels.
4. Low-confidence review: Humans review 100% of <0.80 confidence.
5. Disagreement resolution: If human and AI differ, escalate to expert.
6. Feedback loop: Use disagreements to refine prompt.

Labeling templates for common tasks

Named Entity Recognition (NER):

Extract entities [PERSON, ORG, LOCATION, PRODUCT] as JSON.
Text: "..."
JSON: {...}

Sentiment + Aspect:

Label sentiment [POS/NEG/NEUT] + aspect [Quality/Price/Service].
Respond: label,aspect

Multi-label classification:

Label all applicable categories [AI, Business, Tech, Health].
Respond: comma-separated labels

Frequently asked questions

Q: How do I measure label quality? A: Calculate inter-rater agreement (human vs. AI) on 500 samples. Cohen's Kappa >0.80 = good. Iterate on prompt if <0.75.

Q: Can I use AI-generated labels directly without human review? A: For non-critical tasks (sentiment, topic), yes. For high-stakes (medical, legal, financial), always have humans review 100%.

Q: How do I handle ambiguous or noisy data? A: Add an "Unclear" or "Ambiguous" label. For very noisy data, ask the model to explain reasoning, then filter by confidence.

Q: Can I label data in Hindi, Tamil, or regional languages? A: Yes. Claude Sonnet and Qwen3 models support Indian languages. Test on 50 samples to validate accuracy before scaling.

Q: What if my labels drift over time (e.g., label definitions change)? A: Version your prompt. Store prompt version + output date with every label. When definitions change, re-label the entire dataset with the new prompt.

Q: How do I audit labels for fairness or bias? A: Sample predictions by demographic (if applicable). Check for label imbalance. Ask the model to explain its reasoning; look for stereotypes.

Get started in rupees → https://unoblox.ai/sign-in