The Profanity API

About the startup

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: We 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 Who is this for? Developers building chat, messaging, or social features Gaming companies needing context-aware moderation EdTech platforms requiring child-safe content filtering SaaS builders adding moderation to user-generated content Medical/legal platforms where clinical terms shouldn't trigger false positives AI developers filtering prompts and model outputs API Response Examples: Simple response (balanced mode): json { "flagged": false, "score": 0.23, "intent": "joking", "source": "semantic", "latencyMs": 187, "mode": "smart", "llmUsed": false } With includeLayersDetails: true: json { "flagged": true, "score": 0.94, "intent": "abusive", "source": "llm", "detections": [ { "text": "idiot", "position": { "start": 15, "end": 20 }, "confidence": 0.87, "detectionSource": "dictionary" } ], "layers": { "blocklist": { "ran": true, "durationMs": 1, "matchCount": 0 }, "static": { "ran": true, "durationMs": 3, "matchCount": 0 }, "dictionary": { "ran": true, "durationMs": 45, "matchCount": 1, "topScore": 0.87 }, "semantic": { "ran": true, "durationMs": 112, "matchCount": 0, "topScore": 0.31 }, "llm": { "ran": true, "durationMs": 340, "reasoning": "Direct insult targeting an individual" } }, "latencyMs": 502, "mode": "strict", "llmUsed": true }