Content Moderation API in India

Automate content moderation at scale

Manually reviewing user-generated content doesn't scale past a few thousand posts a day. unoblox lets you deploy AI moderation — flagging spam, abuse, explicit material, and misinformation — through one India-native, OpenAI-compatible endpoint, billed in rupees on a monthly GST invoice.

Why LLM-based moderation beats keyword filters

Rule-based filters catch obvious spam but miss context: sarcasm, coded slurs, misspelled abuse, or evolving slang. A language model reads the full sentence, not just a blocklist match, and can explain why it flagged something — which matters when a human reviewer has to double-check an edge case.

Recommended models

Model	Input ₹/1M	Output ₹/1M	Best for
Qwen3 1.7B	₹0	₹0	High-volume first pass, free tier
GPT-4.1	₹201.6	₹806.4	Nuanced text + reasoning
Claude Sonnet	₹201.6	₹1008	Balanced cost and judgment

Qwen3 1.7B is freemium — free up to the monthly cap, see /models for current limits.

A two-pass moderation pipeline

Most production systems don't send every post to an expensive model. A cheap first pass filters the obvious majority; only borderline content goes to a stronger model.

1. Pass 1 (Qwen3 1.7B, ₹0): score every submission for a flagged boolean and a confidence value.
2. Pass 2 (GPT-4.1 or Claude Sonnet): anything with confidence below 0.7 gets a second opinion with categories and a reason string.
3. Human queue: anything Pass 2 still flags as ambiguous goes to a moderator dashboard.

from openai import OpenAI

client = OpenAI(
    api_key="ub-gw-...",
    base_url="https://api.unoblox.ai/v1"
)

response = client.chat.completions.create(
    model="openai/gpt-4-1",
    messages=[
        {
            "role": "system",
            "content": "You are a content moderator. Return JSON: {\"flagged\": bool, \"categories\": [str], \"confidence\": float, \"reason\": str}"
        },
        {"role": "user", "content": "Check this comment for violations: [user text]"}
    ],
    response_format={"type": "json_object"}
)

print(response.choices[0].message.content)

Common use cases

• Chat and comment moderation on community platforms
• Marketplace listing review (e-commerce, classifieds)
• Social media content screening
• Messaging and DM spam detection
• Toxicity classification for gaming and forums

What it costs at scale

A typical moderation call runs 100–300 tokens. Running the free Qwen3 1.7B first pass on a million posts a month costs ₹0 up to your plan's cap; routing only the small share that need a second opinion to GPT-4.1 keeps a million-post month to a modest rupee spend — check current caps and rates on /models before committing to a volume estimate.

Frequently asked questions

Q: Can moderation handle Hindi and regional languages? A: Yes. GPT-4.1 and Claude Sonnet both read Hindi, English, and most major Indian languages with reasonable cultural context — test on a sample of your own flagged content before trusting it fully.

Q: How fast is moderation at chat speed? A: Typical latency is a few hundred milliseconds per call, fast enough to gate a message before it posts. For bulk historical review, batch requests instead of gating in real time.

Q: Can I moderate images, not just text? A: Yes — GPT-4o reads images directly (explicit, violent, or hateful content). For video, extract key frames and send them as images.

Q: Do I need to build my own classifier? A: No. Start with a zero-shot prompt like the one above, and add few-shot examples from your own flagged history if you need it tuned to your specific policy.

Q: Should AI moderation replace human moderators? A: No — use it to cut the volume humans have to look at, not to remove the human step. Keep a person in the loop for appeals, bans, and anything legally sensitive.

Q: Where is my moderation data processed? A: Requests route through unoblox's India-based gateway and billing; the model you pick determines where the actual inference happens — see each model's page on /models for specifics.

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