multilingual profanity detection and flagging, hate speech classification and categorization, real-time content moderation api with webhook integration, freemium tiered api rate limiting and quota management, false positive and false negative reporting with feedback loops, language detection and multi-language profanity filtering, dashboard-based moderation queue and manual review interface

Fuk.ai

ProductFree

AI-driven profanity and hate speech moderation...

Best for:Small to mid-sized online communities and forums that need affordable automated profanity filtering as a first-line defense but have adequate human moderators for nuanced edge cases.

/ 100

7 capabilities

Capabilities7 decomposed

multilingual profanity detection and flagging

Medium confidence

Detects profanity and offensive language across multiple languages using a combination of lexicon-based matching and pattern recognition. The system maintains language-specific profanity dictionaries and applies tokenization/normalization to catch variations (e.g., leetspeak, character substitutions). Flags detected content with severity scores and returns structured metadata about violation type and language detected.

Solves for

I need to automatically catch profanity in user-generated content across English, Spanish, French, and other languages without manual review of every postI want to filter toxic language in real-time as users submit comments, messages, or forum postsI need to identify and quarantine profane content before it's visible to other community members

Best for

small to mid-sized online communities (forums, Discord servers, comment sections) with 100K-10M monthly messages

global platforms serving multilingual user bases where manual moderation is cost-prohibitive

teams needing a first-line automated filter to reduce manual moderation workload by 60-80%

Requires

API key from Fuk.ai dashboard

HTTP/REST client capability or SDK integration

Text input in UTF-8 encoding

Limitations

Limited context awareness — cannot distinguish between reclaimed slurs, sarcasm, or cultural references, leading to false positives in 5-15% of edge cases

Struggles with coded language, acronyms, and evolving slang that emerges faster than dictionary updates

No semantic understanding of intent — flags the word 'gay' in 'I'm feeling gay today' the same as in hateful contexts

What makes it unique

Maintains language-specific profanity lexicons with normalization for character substitutions and leetspeak variants, rather than relying solely on ML models. This enables fast, deterministic detection with low false negatives for known profanity, though at the cost of missing context-dependent toxicity.

vs alternatives

Faster and cheaper than ML-based competitors (Perspective API, Azure Content Moderator) for high-volume profanity filtering, but lacks semantic understanding of nuanced hate speech and cultural context that those models provide.

hate speech classification and categorization

Medium confidence

Classifies detected toxic content into specific hate speech categories (e.g., racial slurs, religious hate, gender-based harassment, ableist language) using pattern matching and keyword association. Returns structured category tags alongside severity scores, enabling moderators to apply category-specific policies (e.g., auto-remove racial slurs, flag for review on gender harassment).

Solves for

I need to categorize hate speech by type so I can apply different moderation actions (auto-remove vs. flag vs. quarantine) based on severity and categoryI want to track which types of hate speech are most prevalent in my community to inform moderation policy updatesI need to report hate speech incidents by category to legal/compliance teams for documentation

Best for

community managers building tiered moderation policies with category-specific enforcement rules

platforms required to maintain audit trails of hate speech incidents by type for compliance

teams analyzing community health metrics and toxicity trends over time

Requires

API key from Fuk.ai

Text input in supported languages

Backend system to store and query categorized violations

Limitations

Category boundaries are often ambiguous — content targeting multiple groups (e.g., 'disabled immigrant') may be miscategorized or require manual review

No hierarchical categorization — cannot distinguish between mild and severe instances within a category

Categories are likely fixed and predefined, making it difficult to adapt to emerging hate speech targeting new groups

What makes it unique

Uses keyword-to-category mapping with pattern rules to classify hate speech into discrete categories, enabling policy-driven moderation workflows. This is more operationally transparent than black-box ML models but less adaptable to emerging hate speech patterns.

vs alternatives

More transparent and auditable than ML-based classifiers for compliance purposes, but less accurate at detecting novel or subtle hate speech compared to fine-tuned transformer models like those in Perspective API.

real-time content moderation api with webhook integration

Medium confidence

Exposes REST API endpoints for synchronous content submission and asynchronous webhook callbacks for moderation results. Integrates with platforms via HTTP POST requests, processes submissions through the detection pipeline, and returns flagged content metadata. Supports batch processing for historical content and real-time streaming for live user submissions.

Solves for

I want to check user comments for profanity before they're posted to my platformI need to integrate moderation checks into my comment submission workflow without blocking user experienceI want to reprocess historical content (e.g., 1M old comments) to flag previously undetected hate speech

Best for

web platforms (forums, social networks, comment sections) with existing REST API infrastructure

teams building moderation workflows that need to integrate checks at multiple points (submission, display, search)

platforms with both real-time and batch moderation needs

Requires

HTTP client library (curl, axios, requests, etc.)

API key from Fuk.ai dashboard

Publicly accessible webhook endpoint (for async results) or polling mechanism

Limitations

Synchronous API calls add latency to user submission workflows — likely 200-500ms per request, which may impact perceived responsiveness

Freemium tier likely has strict rate limits (e.g., 10-100 requests/second), making batch processing of large datasets slow

No built-in retry logic or circuit breaker — client must handle API failures and implement exponential backoff

What makes it unique

Provides both synchronous API and asynchronous webhook patterns, allowing platforms to choose between blocking (safe but slower) and non-blocking (faster but eventual consistency) moderation workflows. This flexibility is rare in specialized moderation tools.

vs alternatives

Simpler REST API integration compared to competitors requiring custom SDKs or complex authentication schemes, but lacks the performance optimizations (caching, local inference) of on-premise solutions like Detoxify.

freemium tiered api rate limiting and quota management

Medium confidence

Implements usage-based access control with freemium tier quotas (e.g., 10K API calls/month) and paid tier scaling. Tracks API calls per account, enforces rate limits via token bucket or sliding window algorithms, and returns HTTP 429 responses when limits are exceeded. Provides dashboard visibility into usage metrics and quota remaining.

Solves for

I want to pilot Fuk.ai on my small community without paying upfront to validate the tool's effectivenessI need to understand my moderation API costs before scaling to productionI want to set up alerts when my team approaches monthly API quota limits

Best for

startups and small teams evaluating moderation tools with limited budgets

platforms with variable traffic patterns that need flexible, pay-as-you-go pricing

teams piloting moderation solutions before committing to enterprise contracts

Requires

Fuk.ai account with API key

Monitoring system to track API usage and quota remaining

Fallback moderation strategy when API quota is exhausted

Limitations

Freemium quotas are likely too restrictive for production use — 10K calls/month = ~330 calls/day, insufficient for platforms with >1K daily active users

No quota carryover or burst allowance — hitting limits mid-month blocks all moderation until next billing cycle

Paid tier pricing is not transparent in public documentation, making cost estimation difficult

What makes it unique

Freemium model with generous free tier (relative to enterprise competitors) enables low-friction adoption for small communities, but quotas are intentionally restrictive to drive paid tier upgrades. This is a common SaaS pattern but limits utility for scaling platforms.

vs alternatives

More accessible entry point than Perspective API (requires Google Cloud account) or Azure Content Moderator (enterprise-focused), but less flexible than open-source alternatives (Detoxify, Perspective API's open-source models) that have no rate limits.

false positive and false negative reporting with feedback loops

Medium confidence

Allows moderators to report misclassifications (false positives where benign content is flagged, false negatives where toxic content is missed) via API or dashboard. Collects feedback with context (original text, detected category, moderator's correction) and feeds into model retraining or lexicon updates. Tracks feedback metrics to identify systematic biases.

Solves for

I want to report when Fuk.ai incorrectly flags 'gay' in non-hateful contexts so the system learns from my feedbackI need to track how often the tool misses hate speech so I can decide if it's reliable enough for my communityI want to contribute to improving the tool's accuracy by submitting corrections that help retrain the model

Best for

communities committed to improving moderation accuracy over time through active feedback

teams with dedicated moderation staff who can systematically report edge cases

platforms willing to share moderation data (with privacy safeguards) to improve the tool

Requires

Fuk.ai API key with feedback reporting permissions

Dashboard access or API endpoint for submitting corrections

Moderation workflow that captures moderator decisions and reasoning

Limitations

Feedback loops are likely asynchronous — corrections may take weeks or months to appear in production models

No transparency into how feedback is used — unclear if corrections are applied to all users or only the reporting account

Privacy concerns — submitting flagged content and corrections to Fuk.ai may violate user privacy policies or data residency requirements

What makes it unique

Implements a feedback loop mechanism that allows users to contribute corrections, creating a crowdsourced improvement cycle. This is more collaborative than closed-box competitors but requires trust in how feedback is used and stored.

vs alternatives

More transparent and community-driven than proprietary competitors (Perspective API, Azure), but less mature than open-source projects (Detoxify) where users can directly contribute code and retrain models locally.

language detection and multi-language profanity filtering

Medium confidence

Automatically detects the language of input text using character encoding analysis and language identification models, then applies language-specific profanity lexicons and rules. Supports profanity detection across 10+ languages (estimated based on 'multiple language' claim) with language-specific normalization (e.g., diacritics removal for French, character variants for Arabic).

Solves for

I have a global community with users posting in English, Spanish, French, German, and Arabic — I need to detect profanity in all languages automaticallyI want to apply different moderation policies per language (e.g., stricter filtering for languages with less mature moderation tools)I need to identify the language of flagged content for reporting and compliance purposes

Best for

global platforms with diverse user bases across multiple language regions

teams without native speakers for all supported languages who need automated language-aware filtering

platforms required to maintain language-specific moderation audit trails

Requires

Text input in UTF-8 encoding

API key from Fuk.ai

No special configuration — language detection is automatic

Limitations

Language detection is imperfect for code-mixed content (e.g., 'Hola amigo, that's cool') — may misidentify or fail to detect profanity in mixed-language posts

Profanity lexicons vary in quality and completeness across languages — some languages (e.g., English, Spanish) likely have comprehensive coverage, while others (e.g., Tagalog, Vietnamese) may have gaps

No support for transliteration variants (e.g., Cyrillic 'а' vs. Latin 'a') — may miss profanity in intentionally misspelled cross-script text

What makes it unique

Combines automatic language detection with language-specific profanity lexicons, enabling a single API call to handle global content moderation. This is more convenient than competitors requiring explicit language specification or separate API calls per language.

vs alternatives

More convenient than Perspective API (requires explicit language specification) for global platforms, but less accurate than human moderators or fine-tuned multilingual models for nuanced profanity in non-English languages.

dashboard-based moderation queue and manual review interface

Medium confidence

Provides a web dashboard where moderators can view flagged content in a queue, review context (user profile, post history, timestamp), and take actions (approve, remove, escalate, add to blocklist). Integrates with the API to pull flagged items and stores moderator decisions for audit trails and feedback loops.

Solves for

I want a centralized place to review all flagged content from my community and decide whether to remove it or approve itI need to see user context (profile, history) when reviewing flagged content to make better moderation decisionsI want to track which moderators made which decisions for accountability and training purposes

Best for

community managers and moderation teams with 1-10 moderators

platforms needing human-in-the-loop moderation for edge cases and appeals

teams building audit trails for compliance and legal documentation

Requires

Fuk.ai account with dashboard access

Web browser (modern Chrome, Firefox, Safari, Edge)

User account with moderator role permissions

Limitations

Dashboard likely has no customization — cannot adjust columns, filters, or workflows to match specific moderation policies

No bulk action support — moderators must review and action each flagged item individually, limiting throughput for high-volume communities

Limited context visibility — dashboard may not integrate with user profile data, post history, or previous moderation decisions from external systems

What makes it unique

Provides a dedicated moderation dashboard integrated with the API, reducing the need for moderators to build custom tools or use generic ticketing systems. This is more user-friendly than API-only competitors but less flexible than open-source moderation platforms.

vs alternatives

More accessible to non-technical moderators than API-only solutions, but less feature-rich than enterprise moderation platforms (Crisp, Zendesk) that offer advanced workflows, team management, and integrations.

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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Best For

✓small to mid-sized online communities (forums, Discord servers, comment sections) with 100K-10M monthly messages
✓global platforms serving multilingual user bases where manual moderation is cost-prohibitive
✓teams needing a first-line automated filter to reduce manual moderation workload by 60-80%
✓community managers building tiered moderation policies with category-specific enforcement rules
✓platforms required to maintain audit trails of hate speech incidents by type for compliance
✓teams analyzing community health metrics and toxicity trends over time
✓web platforms (forums, social networks, comment sections) with existing REST API infrastructure
✓teams building moderation workflows that need to integrate checks at multiple points (submission, display, search)

Known Limitations

⚠Limited context awareness — cannot distinguish between reclaimed slurs, sarcasm, or cultural references, leading to false positives in 5-15% of edge cases
⚠Struggles with coded language, acronyms, and evolving slang that emerges faster than dictionary updates
⚠No semantic understanding of intent — flags the word 'gay' in 'I'm feeling gay today' the same as in hateful contexts
⚠Freemium tier likely caps at 10K-100K API calls/month, making it impractical for platforms processing >1M messages daily
⚠Category boundaries are often ambiguous — content targeting multiple groups (e.g., 'disabled immigrant') may be miscategorized or require manual review
⚠No hierarchical categorization — cannot distinguish between mild and severe instances within a category

Requirements

API key from Fuk.ai dashboardHTTP/REST client capability or SDK integrationText input in UTF-8 encodingNetwork connectivity to Fuk.ai servers (no on-premise option mentioned)API key from Fuk.aiText input in supported languagesBackend system to store and query categorized violationsHTTP client library (curl, axios, requests, etc.)

Input / Output

Accepts: plain text (user comments, messages, forum posts), raw strings up to ~5KB per request (typical API limits), plain text (user-generated content), JSON payload with text field, optional metadata (user_id, timestamp, language), API requests (counted per call, not per token or character), JSON with: original_text, detected_category, moderator_correction, confidence_level, context, plain text in any supported language, Flagged content from API (pulled automatically or via webhook)

Produces: JSON structured response with: flagged boolean, severity score (0-1), violation categories array, language detected, matched terms list, JSON with: primary_category, secondary_categories array, confidence scores per category, severity_level, JSON response with: is_flagged boolean, severity_score, categories, matched_terms, request_id for tracking, HTTP response headers with: X-RateLimit-Limit, X-RateLimit-Remaining, X-RateLimit-Reset, Acknowledgment response with: feedback_id, status (accepted/rejected), estimated_retraining_date, JSON with: detected_language (ISO 639-1 code), language_confidence_score, profanity_flags (language-specific), Moderator actions (approve, remove, escalate) stored in Fuk.ai backend and optionally synced to external systems via API

UnfragileRank

Adoption15%(25% weight)

Quality44%(25% weight)

Ecosystem15%(10% weight)

Match Graph25%(35% weight)

Freshness100%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: Product

7 capabilities

Visit Fuk.ai→

About

AI-driven profanity and hate speech moderation tool

Unfragile Review

Fuk.ai is a specialized content moderation platform that automates the detection and filtering of profanity and hate speech across digital platforms. While the tool addresses a critical need for community safety, its narrow focus on text-based toxicity makes it most useful as a supplementary moderation layer rather than a comprehensive content governance solution.

Pros

+Streamlines moderation workflows by automatically flagging toxic content, reducing manual review overhead for community managers
+Freemium model allows teams to pilot the tool without upfront investment before committing to paid tiers
+Handles multiple language profanity detection, useful for global platforms with diverse user bases

Cons

-Limited context awareness compared to competitors; struggles with sarcasm, coded language, and cultural nuances that can result in false positives and false negatives
-Freemium tier likely has significant API rate limits and flagging capacity constraints, making it impractical for scaling to large platforms

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Capabilities7 decomposed

multilingual profanity detection and flagging

Medium confidence

Solves for

Best for

small to mid-sized online communities (forums, Discord servers, comment sections) with 100K-10M monthly messages

global platforms serving multilingual user bases where manual moderation is cost-prohibitive

teams needing a first-line automated filter to reduce manual moderation workload by 60-80%

Requires

API key from Fuk.ai dashboard

HTTP/REST client capability or SDK integration

Text input in UTF-8 encoding

Limitations

Limited context awareness — cannot distinguish between reclaimed slurs, sarcasm, or cultural references, leading to false positives in 5-15% of edge cases

Struggles with coded language, acronyms, and evolving slang that emerges faster than dictionary updates

No semantic understanding of intent — flags the word 'gay' in 'I'm feeling gay today' the same as in hateful contexts

What makes it unique

vs alternatives

hate speech classification and categorization

Medium confidence

Solves for

Best for

community managers building tiered moderation policies with category-specific enforcement rules

platforms required to maintain audit trails of hate speech incidents by type for compliance

teams analyzing community health metrics and toxicity trends over time

Requires

API key from Fuk.ai

Text input in supported languages

Backend system to store and query categorized violations

Limitations

Category boundaries are often ambiguous — content targeting multiple groups (e.g., 'disabled immigrant') may be miscategorized or require manual review

No hierarchical categorization — cannot distinguish between mild and severe instances within a category

Categories are likely fixed and predefined, making it difficult to adapt to emerging hate speech targeting new groups

What makes it unique

vs alternatives

real-time content moderation api with webhook integration

Medium confidence

Solves for

Best for

web platforms (forums, social networks, comment sections) with existing REST API infrastructure

teams building moderation workflows that need to integrate checks at multiple points (submission, display, search)

platforms with both real-time and batch moderation needs

Requires

HTTP client library (curl, axios, requests, etc.)

API key from Fuk.ai dashboard

Publicly accessible webhook endpoint (for async results) or polling mechanism

Limitations

Synchronous API calls add latency to user submission workflows — likely 200-500ms per request, which may impact perceived responsiveness

Freemium tier likely has strict rate limits (e.g., 10-100 requests/second), making batch processing of large datasets slow

No built-in retry logic or circuit breaker — client must handle API failures and implement exponential backoff

What makes it unique

vs alternatives

freemium tiered api rate limiting and quota management

Medium confidence

Solves for

Best for

startups and small teams evaluating moderation tools with limited budgets

platforms with variable traffic patterns that need flexible, pay-as-you-go pricing

teams piloting moderation solutions before committing to enterprise contracts

Requires

Fuk.ai account with API key

Monitoring system to track API usage and quota remaining

Fallback moderation strategy when API quota is exhausted

Limitations

Freemium quotas are likely too restrictive for production use — 10K calls/month = ~330 calls/day, insufficient for platforms with >1K daily active users

No quota carryover or burst allowance — hitting limits mid-month blocks all moderation until next billing cycle

Paid tier pricing is not transparent in public documentation, making cost estimation difficult

What makes it unique

vs alternatives

false positive and false negative reporting with feedback loops

Medium confidence

Solves for

Best for

communities committed to improving moderation accuracy over time through active feedback

teams with dedicated moderation staff who can systematically report edge cases

platforms willing to share moderation data (with privacy safeguards) to improve the tool

Requires

Fuk.ai API key with feedback reporting permissions

Dashboard access or API endpoint for submitting corrections

Moderation workflow that captures moderator decisions and reasoning

Limitations

Feedback loops are likely asynchronous — corrections may take weeks or months to appear in production models

No transparency into how feedback is used — unclear if corrections are applied to all users or only the reporting account

Privacy concerns — submitting flagged content and corrections to Fuk.ai may violate user privacy policies or data residency requirements

What makes it unique

vs alternatives

language detection and multi-language profanity filtering

Medium confidence

Solves for

Best for

global platforms with diverse user bases across multiple language regions

teams without native speakers for all supported languages who need automated language-aware filtering

platforms required to maintain language-specific moderation audit trails

Requires

Text input in UTF-8 encoding

API key from Fuk.ai

No special configuration — language detection is automatic

Limitations

Language detection is imperfect for code-mixed content (e.g., 'Hola amigo, that's cool') — may misidentify or fail to detect profanity in mixed-language posts

No support for transliteration variants (e.g., Cyrillic 'а' vs. Latin 'a') — may miss profanity in intentionally misspelled cross-script text

What makes it unique

vs alternatives

dashboard-based moderation queue and manual review interface

Medium confidence

Solves for

Best for

community managers and moderation teams with 1-10 moderators

platforms needing human-in-the-loop moderation for edge cases and appeals

teams building audit trails for compliance and legal documentation

Requires

Fuk.ai account with dashboard access

Web browser (modern Chrome, Firefox, Safari, Edge)

User account with moderator role permissions

Limitations

Dashboard likely has no customization — cannot adjust columns, filters, or workflows to match specific moderation policies

No bulk action support — moderators must review and action each flagged item individually, limiting throughput for high-volume communities

Limited context visibility — dashboard may not integrate with user profile data, post history, or previous moderation decisions from external systems

What makes it unique

vs alternatives

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Unfragile Review

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