The problem with existing content moderation:

Every developer who's built profanity filtering knows the frustration. Traditional filters either flag "assistant" because it contains "ass," or miss obviously toxic content because they're just matching keywords. They can't tell the difference between "I'll destroy you in this game" (friendly trash talk) and an actual threat.

How The Profanity API solves this:

I built a 5-layer detection pipeline that analyzes content progressively:

Blocklist (L0): Your custom blocklist for instant flagging

Static (L1): Exact match against known profanity

Dictionary (L2): Semantic word matching that catches meaning, not just spelling

Semantic (L3): Phrase-level understanding for context-aware detection

LLM (L4): Human-like judgment for edge cases

The key insight: layer disagreement

When a word gets flagged by keyword matching but scores low on semantic analysis, that disagreement signals potentially innocent usage. Instead of defaulting to "block," we escalate to LLM analysis. This is how we catch "I'll kill this presentation" as safe while still flagging subtle threats.

5 analysis modes to match your needs:

instant - Blocklist + exact matching only, sub-5ms for high-volume chat

fast - Catches obfuscation attempts (f*ck, a$$hole)

balanced - Semantic + context awareness (default)

strict - Full pipeline with LLM, maximum accuracy

smart - Adaptive routing that only uses LLM when needed

12 context types for environment-aware detection:

Different environments need different standards:

Lenient contexts (higher threshold, more forgiving):

gaming - "I'll destroy you" is friendly banter

creative - Fiction, roleplay, storytelling

educational - Academic discussion of sensitive topics

medical - Clinical terminology without false positives

legal - Court documents, policy discussions

Standard contexts:

chat - Real-time messaging

comment - Comment sections

review - Product/service reviews

bio - User profiles

Strict contexts (lower threshold, less forgiving):

professional - Workplace communications

child_safe - Zero tolerance for children's platforms

username - Display names (strictest on slurs)

12 intent categories for nuanced decisions:

We don't just say "profane or not." We classify what the person is actually doing:

safe - Clean content

joking - Friendly banter, humor, gaming trash talk

venting_situational - Frustration at things, not people ("fuck this traffic!")

venting_targeted - Frustration directed at someone

passive_aggressive - Indirect hostility, backhanded compliments

discriminatory - Bias against groups (race, gender, religion, etc.)

abusive - Direct harassment, slurs, insults

threatening - Explicit or veiled threats

sexual - Sexual content, innuendo, unwanted advances

quoting - Referencing others' words or media

educational - Academic discussion of sensitive topics

professional - Medical, legal, or technical language in context

Built for developers:

RESTful API with comprehensive docs

Custom allowlist for domain-specific terms (e.g., "weed" in gardening forums)

Custom blocklist for terms you always want flagged

Optional contextDescription for LLM (like "Medical triage log from ER")

includeLayersDetails flag for debugging and transparency

Smart pricing: pay base rate for standard calls, LLM surcharge only when it's actually used

Key Features:

5-layer detection pipeline: blocklist → static → dictionary → semantic → LLM

12 context types and 12 intent categories

Smart mode only runs LLM when layers disagree

Custom allowlist/blocklist per request

Detailed layer-by-layer breakdown with match positions

Sub-15ms response time in instant mode

3-1250 character limit per request

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