{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_brandwise-ai","slug":"brandwise-ai","name":"Brandwise AI","type":"product","url":"https://brandwise.ai","page_url":"https://unfragile.ai/brandwise-ai","categories":["text-writing"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_brandwise-ai__cap_0","uri":"capability://safety.moderation.real.time.social.media.comment.classification.and.toxicity.detection","name":"real-time social media comment classification and toxicity detection","description":"Analyzes incoming social media comments across multiple platforms using machine learning models trained to identify brand-damaging language patterns, including insults, complaints, misinformation, and trolling. The system processes comments in real-time as they're posted, classifying them by severity and damage potential before they accumulate engagement. Uses multi-platform API integrations (Facebook Graph API, Twitter API, Instagram Graph API, TikTok API) to ingest comment streams and applies ensemble classification models to reduce false positives while maintaining high recall on genuinely harmful content.","intents":["I need to automatically flag negative comments before they go viral and damage my brand reputation","I want to identify which comments are trolls vs legitimate customer complaints so I can respond appropriately","I need to monitor comments 24/7 across all my social channels without hiring a dedicated team"],"best_for":["Mid-market e-commerce brands receiving 100+ comments daily across multiple platforms","Consumer brands with high social media visibility managing reputation at scale","Community managers lacking 24/7 coverage who need automated first-pass filtering"],"limitations":["Classification accuracy degrades on sarcasm, context-dependent criticism, and regional slang — may over-filter legitimate feedback","Requires 7-14 day training period on brand-specific comment history to tune toxicity thresholds, reducing effectiveness on day-one deployment","Multi-language support limited to top 15 languages; regional dialects and code-switching may cause misclassification","Latency of 2-5 seconds per comment means fast-moving viral threads may accumulate 50+ comments before first detection"],"requires":["Active social media accounts with API access enabled (Facebook Business Account, Twitter Developer Account, Instagram Business Account)","Minimum 500 historical comments for model fine-tuning; cold-start performance is poor without baseline data","Webhook or polling infrastructure to receive real-time comment streams from social platforms"],"input_types":["text (comment body)","metadata (commenter profile, engagement metrics, timestamp, platform source)"],"output_types":["structured classification (toxicity score 0-100, damage category, confidence level)","action recommendation (hide, flag for review, suppress from feed)"],"categories":["safety-moderation","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_brandwise-ai__cap_1","uri":"capability://automation.workflow.automated.comment.suppression.and.visibility.control.across.platforms","name":"automated comment suppression and visibility control across platforms","description":"Automatically hides, deletes, or deprioritizes flagged comments on social media platforms using native platform APIs and moderation webhooks. The system applies suppression rules based on classification results — comments above a toxicity threshold are immediately hidden from public view, moved to a moderation queue, or deleted entirely depending on configured policies. Integrates with platform-native moderation tools (Facebook Comment Moderation API, Twitter Mute/Block APIs, Instagram Comment Controls) to execute suppression without requiring manual intervention, maintaining an audit log of all actions for compliance and review.","intents":["I want negative comments hidden automatically so they don't accumulate likes and replies that amplify the damage","I need to remove spam and trolling comments instantly without waiting for my team to review them","I want to maintain a curated, positive-looking comment section that protects brand perception"],"best_for":["Brands prioritizing reputation management and perception control over authentic engagement","High-volume social accounts (1000+ comments/day) where manual moderation is operationally infeasible","E-commerce and consumer brands where negative comments directly impact purchase decisions"],"limitations":["Suppressed comments remain visible to the original commenter, creating perception of censorship if they notice — may trigger backlash on Twitter/Reddit where suppression is more visible","Platform API rate limits restrict suppression speed — Facebook allows ~200 moderation actions/minute, causing queuing during viral moments","No ability to suppress comments on third-party platforms (Reddit, news sites, forums) — only controls owned social channels","Suppression creates no record of the complaint for product teams; legitimate feedback is lost unless separately logged","Reputational risk: audiences increasingly expect transparent responses to criticism; mass suppression can appear evasive and trigger 'cover-up' narratives"],"requires":["Admin/moderator access to social media accounts with API permissions enabled","OAuth tokens with 'manage_pages' (Facebook), 'tweet.moderate.write' (Twitter), 'instagram_graph_api' (Instagram) scopes","Configured moderation policies defining suppression thresholds and actions (hide vs delete vs queue)"],"input_types":["classification results (toxicity score, damage category)","moderation policy rules (if toxicity > 75, then delete; if 50-75, then hide)"],"output_types":["suppression action confirmation (comment hidden, deleted, or queued)","audit log entry (timestamp, comment ID, action taken, reason)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_brandwise-ai__cap_10","uri":"capability://data.processing.analysis.commenter.profile.analysis.and.bad.faith.actor.detection","name":"commenter profile analysis and bad-faith actor detection","description":"Analyzes commenter profiles to identify patterns of bad-faith engagement (trolls, competitors, coordinated attacks, spam bots) and applies different suppression rules based on commenter type. The system examines commenter history (previous comments, engagement patterns, account age, follower count), network patterns (whether commenter is part of coordinated attack), and behavioral signals (rapid-fire commenting, cross-posting identical comments). Enables suppression of comments from known bad-faith actors even if individual comments are not inherently damaging, and conversely, may suppress less aggressively for comments from loyal customers or verified accounts.","intents":["I want to suppress comments from trolls and spam bots even if individual comments aren't inherently damaging","I need to detect coordinated attacks where multiple accounts post similar complaints to amplify damage","I want to suppress less aggressively for comments from loyal customers or verified accounts"],"best_for":["Brands receiving high volumes of trolling and spam comments","Brands in competitive industries where competitors may coordinate negative campaigns","Brands with large, engaged communities where distinguishing loyal customers from bad-faith actors is valuable"],"limitations":["Commenter profile analysis risks discriminating against legitimate users — suppressing comments from new accounts or low-follower-count users may silence genuine customers who happen to be new to social media","Bad-faith actor detection is prone to false positives — a user posting multiple similar comments may be genuinely concerned, not coordinating an attack","Commenter profile data is incomplete — social platforms don't expose full user history or network graphs, limiting visibility into coordinated attacks","Suppressing based on commenter identity (rather than comment content) is ethically problematic and may violate platform terms of service","Commenter profiles change over time — a user who was a troll may become a loyal customer, but the system may continue suppressing their comments based on historical profile"],"requires":["Access to commenter profile data (account age, follower count, comment history, engagement patterns)","Commenter classification model trained to identify bad-faith actors","Network analysis to detect coordinated attacks (multiple accounts posting similar content)"],"input_types":["commenter profile (account age, follower count, comment history, engagement patterns)","comment content and metadata (timestamp, engagement, cross-posting patterns)"],"output_types":["commenter classification (loyal customer, new user, troll, bot, competitor, coordinated attacker)","suppression recommendation (suppress based on commenter type, not just comment content)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_brandwise-ai__cap_11","uri":"capability://tool.use.integration.integration.with.social.platform.native.moderation.tools.and.appeals.workflows","name":"integration with social platform native moderation tools and appeals workflows","description":"Integrates with native platform moderation tools (Facebook Comment Moderation API, Twitter Mute/Block APIs, Instagram Comment Controls) to execute suppression decisions through official channels rather than workarounds. Also integrates with platform appeals workflows, enabling users whose comments were suppressed to appeal through official platform mechanisms, and routing appeals back to Brandwise for review. The system maintains synchronization between Brandwise suppression decisions and platform-native moderation state, ensuring consistency across systems. Enables brands to use Brandwise as the decision engine while leveraging platform-native enforcement and appeals infrastructure.","intents":["I want to suppress comments using official platform APIs rather than workarounds that might violate terms of service","I need to handle appeals from users whose comments were suppressed, and I want to use platform-native appeals workflows","I want to ensure my suppression decisions are consistent across Brandwise and platform-native moderation tools"],"best_for":["Brands concerned about compliance with platform terms of service","Brands wanting to use official platform moderation infrastructure rather than custom solutions","Brands needing to handle appeals through official platform mechanisms"],"limitations":["Platform API capabilities vary — some platforms (Facebook) have robust moderation APIs, others (TikTok) have limited API access, limiting consistency across platforms","Platform API rate limits restrict suppression speed — cannot suppress all comments instantly, causing queueing during high-volume periods","Appeals workflows are platform-specific — each platform has different appeals processes, requiring custom integration for each platform","Synchronization between Brandwise and platform-native moderation state can drift — if suppression is done outside Brandwise (e.g., manual moderation), Brandwise loses visibility","Platform API changes can break integrations — platforms frequently update APIs, requiring ongoing maintenance"],"requires":["API credentials and OAuth tokens for each social platform with moderation API access","Integration with platform-native appeals workflows (webhooks or polling for appeal notifications)","Synchronization mechanism to keep Brandwise and platform-native moderation state consistent"],"input_types":["suppression decisions from Brandwise (comment ID, action, reason)","appeals from platform-native workflows (appeal notification, commenter identity, original comment)"],"output_types":["suppression execution through platform APIs (comment hidden, deleted, or queued)","appeal routing to Brandwise for review","synchronization status (Brandwise state vs platform-native state)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_brandwise-ai__cap_2","uri":"capability://data.processing.analysis.multi.platform.social.media.monitoring.and.comment.stream.aggregation","name":"multi-platform social media monitoring and comment stream aggregation","description":"Continuously ingests comment streams from multiple social platforms (Facebook, Twitter, Instagram, TikTok, LinkedIn) using platform-specific APIs and webhooks, normalizing them into a unified data model for processing. The system maintains persistent connections to platform APIs (using webhooks where available, polling as fallback) to capture comments in real-time, deduplicates cross-platform mentions of the same brand, and enriches comments with metadata (commenter profile, engagement metrics, platform source, timestamp). Aggregation enables single-pane-of-glass monitoring across fragmented social presence without requiring manual platform switching.","intents":["I need to monitor comments across all my social channels in one place instead of checking each platform separately","I want to see which platforms are generating the most negative sentiment about my brand","I need to detect coordinated attacks or trending complaints across multiple platforms simultaneously"],"best_for":["Multi-channel brands with presence on 3+ social platforms","Brands needing centralized monitoring dashboard for distributed community management teams","Crisis management teams tracking real-time sentiment spikes across all channels"],"limitations":["API rate limits vary by platform — Twitter allows 450 requests/15min, Facebook 200/minute, causing uneven ingestion speed and potential comment lag during traffic spikes","Platform API access requires separate approval for each channel; TikTok API access is restricted and requires business partnership, limiting coverage","Webhook delivery is not guaranteed by platforms — Facebook webhooks have ~99.9% delivery but occasional comments may be missed during platform outages","Comment enrichment metadata varies by platform — Twitter provides engagement metrics in real-time, Instagram requires separate API call per comment, adding latency","Historical comment backfill is limited — most platforms only provide 7-30 days of comment history via API, preventing analysis of long-term trends"],"requires":["API credentials and OAuth tokens for each social platform (Facebook App ID/Secret, Twitter Bearer Token, Instagram Business Account, TikTok API access)","Webhook infrastructure to receive real-time notifications (HTTPS endpoint, signature verification, retry handling)","Database to store normalized comment data (minimum 100GB for 6 months of comments at 1000 comments/day scale)"],"input_types":["platform-specific API responses (JSON from Facebook Graph API, Twitter API v2, Instagram Graph API)","webhook payloads (real-time comment notifications)"],"output_types":["normalized comment objects (unified schema across platforms)","aggregated metadata (engagement counts, sentiment, platform source, commenter profile)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_brandwise-ai__cap_3","uri":"capability://data.processing.analysis.brand.specific.damage.severity.scoring.and.prioritization","name":"brand-specific damage severity scoring and prioritization","description":"Assigns numerical damage scores (0-100) to flagged comments based on brand-specific impact models that weight different types of criticism differently. The system learns which comment patterns cause the most reputational harm for each brand — for example, product quality complaints may score higher for a luxury brand than for a budget brand, and safety concerns always score high regardless of brand. Uses logistic regression or gradient boosting models trained on historical comment data labeled by brand teams, enabling prioritization of suppression and review efforts on the highest-impact comments. Damage scores drive both automated suppression thresholds and manual review queue ordering.","intents":["I want to suppress the most damaging comments first and deprioritize minor complaints that don't hurt my brand","I need to understand which types of criticism are most harmful to my specific brand so I can address root causes","I want my moderation team to focus on high-impact comments instead of wasting time on low-severity spam"],"best_for":["Brands with distinct brand positioning where damage severity varies by comment type (luxury vs budget, B2B vs B2C, etc.)","Mature brands with 6+ months of comment history to train damage models","Brands with dedicated community management teams who can label training data"],"limitations":["Damage scoring requires labeled training data — brands must manually review 500-1000 comments and assign damage labels, creating 10-20 hour setup burden","Model accuracy degrades over time as brand perception shifts; models require retraining every 3-6 months to stay calibrated","Damage scoring is subjective and reflects brand team's bias — a comment labeled 'high damage' by one team member may be labeled 'low damage' by another, reducing model reliability","Scoring cannot account for context outside the comment (e.g., commenter's follower count, timing relative to news cycle) — a complaint from a micro-influencer scores same as from a regular user","False negatives are costly — a high-damage comment scored as low-damage may go unsuppressed and cause significant reputational harm"],"requires":["Minimum 500 labeled comments with damage severity scores (0-100) for model training","Brand team availability for 2-4 hour labeling session to create training data","Feedback loop to continuously improve model — requires monthly review of misclassified comments"],"input_types":["comment text and metadata (engagement metrics, commenter profile, platform)","brand-specific damage labels (0-100 severity score assigned by brand team)"],"output_types":["damage severity score (0-100)","damage category (product quality, safety concern, pricing complaint, competitor mention, etc.)","confidence level (0-100) indicating model certainty"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_brandwise-ai__cap_4","uri":"capability://automation.workflow.moderation.policy.configuration.and.rule.based.action.automation","name":"moderation policy configuration and rule-based action automation","description":"Enables brands to define custom moderation policies that automatically trigger suppression, deletion, or review queue actions based on comment classification results. Policies are expressed as conditional rules (e.g., 'if damage_score > 75 AND engagement > 10 likes, then delete; else if damage_score > 50, then hide') and are evaluated in real-time as comments are classified. The system supports policy versioning, A/B testing of different suppression thresholds, and audit logging of all policy changes. Policies can be time-based (e.g., suppress more aggressively during product launches) or audience-based (e.g., suppress differently for verified accounts vs regular users).","intents":["I want to define exactly which comments get suppressed vs hidden vs sent to my team for review","I need to adjust suppression aggressiveness during crisis situations without manually reviewing every comment","I want to test different suppression strategies (aggressive vs conservative) to find the right balance for my brand"],"best_for":["Brands with clear moderation philosophies and defined escalation procedures","Community managers who want control over suppression behavior without coding","Brands running A/B tests to optimize suppression thresholds"],"limitations":["Policy complexity is limited to boolean logic and simple thresholds — cannot express complex contextual rules like 'suppress if commenter is competitor account AND comment mentions pricing'","Policy evaluation latency adds 50-200ms per comment as rules are evaluated sequentially; deeply nested rules (10+ conditions) may cause noticeable delays","No built-in policy versioning or rollback — if a policy causes over-suppression, reverting requires manual policy edit and re-evaluation of already-suppressed comments","A/B testing requires manual traffic splitting and result analysis — no built-in statistical significance testing","Policies cannot account for real-time context like trending topics or competitor activity — rules are static until manually updated"],"requires":["Access to policy configuration UI or API","Understanding of comment classification outputs (damage_score, category, engagement metrics) to write effective rules","Baseline moderation data to inform policy thresholds (e.g., what damage_score threshold separates harmful from harmless comments)"],"input_types":["policy rule definitions (conditional logic: if X > threshold, then action)","comment classification results (damage_score, category, engagement metrics)"],"output_types":["action decision (suppress, delete, hide, queue for review)","policy execution log (which rule matched, why action was taken)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_brandwise-ai__cap_5","uri":"capability://automation.workflow.moderation.queue.and.manual.review.workflow","name":"moderation queue and manual review workflow","description":"Routes flagged comments to a prioritized review queue where community managers can manually approve suppression decisions, provide feedback to improve automated classification, and handle edge cases that the AI cannot confidently classify. Comments are queued based on damage severity, engagement metrics, and policy-defined escalation rules. The review interface displays comment context (original post, commenter profile, engagement history), classification rationale (why the AI flagged it), and suggested action (suppress, delete, or approve). Reviewer feedback is logged and used to retrain classification models, creating a human-in-the-loop learning loop.","intents":["I need my team to review high-risk suppression decisions before they're executed to avoid over-censoring legitimate feedback","I want to understand why the AI flagged certain comments so I can improve the system over time","I need a central dashboard where my team can manage all pending moderation decisions across all platforms"],"best_for":["Brands prioritizing accuracy over speed and willing to accept 5-30 minute review latency","Community management teams with 2+ people who can share review responsibilities","Brands concerned about reputational risk from over-suppression and wanting human oversight"],"limitations":["Manual review introduces latency — comments sit in queue for 5-30 minutes before action, allowing damaging comments to accumulate engagement during review window","Reviewer consistency is poor — different team members may make different suppression decisions on similar comments, reducing model training signal quality","Reviewer burnout is common — high-volume brands (1000+ comments/day) require multiple reviewers, and reviewing 100+ comments/day is cognitively exhausting","Feedback loop is slow — retraining models on reviewer feedback takes 24-48 hours, so system doesn't immediately improve from corrections","No built-in reviewer performance metrics — cannot measure which reviewers are most accurate or identify training needs"],"requires":["Community management team with 2+ people for sustainable coverage","Review queue UI with comment context, classification rationale, and action buttons","Feedback logging system to capture reviewer decisions and use for model retraining"],"input_types":["flagged comments with classification results (damage_score, category, confidence)","comment context (original post, commenter profile, engagement metrics)","reviewer feedback (approve suppression, reject suppression, provide correction)"],"output_types":["moderation decision (suppress, delete, approve, escalate)","feedback log entry (reviewer decision, timestamp, rationale)","retraining signal (comment + label for model improvement)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_brandwise-ai__cap_6","uri":"capability://data.processing.analysis.sentiment.analysis.and.brand.perception.tracking","name":"sentiment analysis and brand perception tracking","description":"Analyzes the overall sentiment of comments across time periods and segments (by platform, product, commenter type) to track brand perception trends and identify emerging reputation issues. The system classifies comments as positive, negative, or neutral, aggregates sentiment scores over time windows (hourly, daily, weekly), and generates trend reports showing sentiment trajectory. Sentiment analysis is distinct from damage detection — a comment can be negative in sentiment but low in damage (e.g., 'your product is expensive' is negative but not necessarily damaging), or positive in sentiment but high in damage (e.g., 'I love your brand but your customer service is terrible'). Enables brands to understand whether suppression efforts are improving perceived sentiment and to identify which product lines or campaigns are generating the most negative feedback.","intents":["I want to track whether my brand's overall sentiment is improving or declining over time","I need to identify which products or campaigns are generating the most negative feedback","I want to measure the impact of my suppression strategy on perceived brand sentiment"],"best_for":["Brands with mature social media presence (6+ months of comment history) for trend analysis","Marketing and product teams who need data on customer sentiment to inform strategy","Brands measuring ROI of reputation management efforts"],"limitations":["Sentiment analysis accuracy is 75-85% on English comments; degrades significantly on sarcasm, mixed sentiment, and non-English languages","Suppressed comments are excluded from sentiment analysis, creating selection bias — sentiment trends may appear artificially positive because negative comments are hidden","Sentiment aggregation masks important outliers — a single viral complaint from an influencer may be statistically insignificant but reputationally critical","Sentiment trends lag real-world events by 24-48 hours due to comment ingestion latency and batch processing","No causal analysis — sentiment trends show correlation with events (e.g., product launch) but cannot prove causation"],"requires":["Minimum 1000 comments for meaningful sentiment trend analysis","Comment data with timestamps and platform source for segmentation","Sentiment classification model (pre-trained or custom-trained on brand comments)"],"input_types":["comment text and metadata (timestamp, platform, product mentioned, commenter type)"],"output_types":["sentiment classification (positive, negative, neutral, mixed)","sentiment score (-1.0 to +1.0)","trend report (sentiment over time, by segment)","anomaly alerts (sudden sentiment shifts)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_brandwise-ai__cap_7","uri":"capability://automation.workflow.compliance.and.audit.logging.for.moderation.actions","name":"compliance and audit logging for moderation actions","description":"Maintains immutable audit logs of all moderation actions (suppression, deletion, review decisions) with full context (comment content, classification results, policy applied, reviewer identity, timestamp). Logs are designed for compliance with platform terms of service, legal discovery, and internal audits. The system generates compliance reports showing suppression rates by category, false positive rates, and reviewer performance metrics. Enables brands to demonstrate that moderation decisions were made according to defined policies and not arbitrarily, supporting defense against accusations of censorship or bias.","intents":["I need to prove that my suppression decisions were made fairly and according to defined policies, not arbitrary censorship","I need to comply with platform terms of service that require documented moderation decisions","I want to audit my moderation team's performance and identify potential bias in review decisions"],"best_for":["Brands in regulated industries (finance, healthcare) with strict compliance requirements","Brands facing public scrutiny or legal challenges to moderation decisions","Enterprises with governance requirements for content moderation"],"limitations":["Audit logs create liability — detailed records of suppression decisions can be used in legal discovery to argue that brand was deliberately censoring criticism","Log storage is expensive at scale — 1000 comments/day generates ~500KB of audit logs daily, requiring ~150GB/year of storage","Audit logs do not prove fairness — logging that a comment was suppressed according to policy does not prove the policy itself is fair or unbiased","Reviewer identity logging creates privacy concerns — team members may be uncomfortable with detailed tracking of their moderation decisions","Compliance reports are static snapshots — cannot dynamically query logs to answer ad-hoc compliance questions"],"requires":["Immutable log storage (database with append-only writes, or write-once storage like S3 with versioning disabled)","Log retention policy (how long to keep logs before archival)","Access controls to prevent unauthorized log modification or deletion"],"input_types":["moderation action (suppress, delete, approve, escalate)","context (comment content, classification results, policy applied, reviewer identity, timestamp)"],"output_types":["audit log entry (immutable record of action and context)","compliance report (suppression rates, false positive rates, reviewer metrics)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_brandwise-ai__cap_8","uri":"capability://data.processing.analysis.false.positive.detection.and.suppression.accuracy.monitoring","name":"false positive detection and suppression accuracy monitoring","description":"Monitors the accuracy of automated suppression decisions by tracking comments that were suppressed but later determined to be legitimate (false positives). The system uses multiple signals to detect false positives: reviewer feedback during manual review, commenter appeals (users reporting that their comment was wrongly suppressed), engagement metrics (comments that were suppressed but received high engagement from other users, indicating they were valuable), and periodic audits of suppressed comments. Generates accuracy metrics (precision, recall, F1 score) and alerts when false positive rate exceeds thresholds, triggering model retraining or policy adjustment.","intents":["I want to know how often my suppression system is making mistakes and suppressing legitimate comments","I need to detect when my suppression policy is too aggressive and over-filtering valuable feedback","I want to improve my suppression accuracy over time by learning from false positives"],"best_for":["Brands concerned about over-suppression and willing to invest in accuracy monitoring","Brands with active community feedback mechanisms (appeals, reports) that surface false positives","Mature moderation programs with 6+ months of history to establish baseline accuracy"],"limitations":["False positive detection is incomplete — many false positives go undetected because suppressed comments are invisible to most users and never reported","Feedback bias — only engaged users report false positives; casual users may not notice or care that their comment was suppressed","Accuracy metrics are lagging indicators — by the time false positive rate is detected, damage may already be done (e.g., legitimate customer complaint was suppressed and never addressed)","Accuracy monitoring adds overhead — requires manual review of suppressed comments or appeals infrastructure to surface false positives","No ground truth for accuracy — determining whether a comment is 'truly' legitimate or 'truly' damaging is subjective and depends on brand values"],"requires":["Mechanism to surface false positives (reviewer feedback, commenter appeals, periodic audits)","Baseline accuracy metrics from initial model training to compare against","Feedback loop to retrain models on detected false positives"],"input_types":["suppression decisions (comment ID, action taken, timestamp)","false positive signals (reviewer feedback, appeals, engagement metrics)"],"output_types":["accuracy metrics (precision, recall, F1 score, false positive rate)","false positive alerts (when FP rate exceeds threshold)","retraining signals (false positive comments for model improvement)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_brandwise-ai__cap_9","uri":"capability://automation.workflow.crisis.mode.and.surge.suppression.with.dynamic.thresholds","name":"crisis mode and surge suppression with dynamic thresholds","description":"Enables brands to activate 'crisis mode' during reputation emergencies (product recalls, PR scandals, viral complaints) that automatically tightens suppression thresholds and increases suppression aggressiveness. In crisis mode, the system lowers damage score thresholds (e.g., from 75 to 50), increases suppression speed (prioritizes speed over accuracy), and may suppress entire comment threads or disable comments on specific posts. The system can also detect crisis situations automatically by monitoring for sudden spikes in negative sentiment, viral complaints, or mentions of specific keywords (e.g., 'recall', 'lawsuit', 'scandal'). Crisis mode is time-limited and automatically reverts to normal suppression after a configured duration or manual override.","intents":["I need to suppress comments more aggressively during a PR crisis to prevent viral spread of damaging information","I want the system to automatically detect when a crisis is happening and tighten suppression without manual intervention","I need to disable comments entirely on specific posts during emergencies"],"best_for":["Brands in high-risk industries (food, pharma, automotive) where product issues can escalate to crises quickly","Brands with large social media presence where viral complaints can cause significant damage","Brands with crisis management teams who can activate crisis mode quickly"],"limitations":["Crisis mode suppression is highly aggressive and likely to over-suppress legitimate feedback and customer concerns, creating perception of censorship","Automatic crisis detection is prone to false positives — a trending hashtag or news story can trigger crisis mode even if brand is not actually in crisis","Suppressing comments during a crisis can backfire — audiences expect transparency during emergencies, and mass suppression can trigger 'cover-up' narratives and amplify the crisis","Crisis mode disables normal moderation safeguards (accuracy monitoring, reviewer approval) in favor of speed, increasing risk of suppressing valuable feedback","Reverting from crisis mode is manual — if crisis mode is activated too long, normal suppression may resume while crisis is still ongoing, or vice versa"],"requires":["Crisis mode configuration (suppression thresholds, duration, auto-detection keywords)","Ability to manually activate/deactivate crisis mode","Monitoring for crisis signals (sentiment spikes, keyword mentions, engagement anomalies)"],"input_types":["crisis trigger (manual activation or automatic detection via sentiment spike/keyword match)","crisis configuration (suppression thresholds, duration, scope)"],"output_types":["crisis mode status (active/inactive, duration remaining)","suppression actions (comments suppressed under crisis thresholds)","crisis alerts (crisis detected, crisis ended)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["Active social media accounts with API access enabled (Facebook Business Account, Twitter Developer Account, Instagram Business Account)","Minimum 500 historical comments for model fine-tuning; cold-start performance is poor without baseline data","Webhook or polling infrastructure to receive real-time comment streams from social platforms","Admin/moderator access to social media accounts with API permissions enabled","OAuth tokens with 'manage_pages' (Facebook), 'tweet.moderate.write' (Twitter), 'instagram_graph_api' (Instagram) scopes","Configured moderation policies defining suppression thresholds and actions (hide vs delete vs queue)","Access to commenter profile data (account age, follower count, comment history, engagement patterns)","Commenter classification model trained to identify bad-faith actors","Network analysis to detect coordinated attacks (multiple accounts posting similar content)","API credentials and OAuth tokens for each social platform with moderation API access"],"failure_modes":["Classification accuracy degrades on sarcasm, context-dependent criticism, and regional slang — may over-filter legitimate feedback","Requires 7-14 day training period on brand-specific comment history to tune toxicity thresholds, reducing effectiveness on day-one deployment","Multi-language support limited to top 15 languages; regional dialects and code-switching may cause misclassification","Latency of 2-5 seconds per comment means fast-moving viral threads may accumulate 50+ comments before first detection","Suppressed comments remain visible to the original commenter, creating perception of censorship if they notice — may trigger backlash on Twitter/Reddit where suppression is more visible","Platform API rate limits restrict suppression speed — Facebook allows ~200 moderation actions/minute, causing queuing during viral moments","No ability to suppress comments on third-party platforms (Reddit, news sites, forums) — only controls owned social channels","Suppression creates no record of the complaint for product teams; legitimate feedback is lost unless separately logged","Reputational risk: audiences increasingly expect transparent responses to criticism; mass suppression can appear evasive and trigger 'cover-up' narratives","Commenter profile analysis risks discriminating against legitimate users — suppressing comments from new accounts or low-follower-count users may silence genuine customers who happen to be new to social media","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.3333333333333333,"quality":0.74,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:29.715Z","last_scraped_at":"2026-04-05T13:23:42.552Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=brandwise-ai","compare_url":"https://unfragile.ai/compare?artifact=brandwise-ai"}},"signature":"RerQu6j4N41wMlBfqFelKRfSN1Thowf/Ac3PIwd2ODdSORkBeP3b3tEONsgC/vPpPLj5EO0SsIV0ia45K9+9DA==","signedAt":"2026-06-21T09:20:04.425Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/brandwise-ai","artifact":"https://unfragile.ai/brandwise-ai","verify":"https://unfragile.ai/api/v1/verify?slug=brandwise-ai","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}