Brandwise AI

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.

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.

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.

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.

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.

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.

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).

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.

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.

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.

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.

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.