The AI brand safety risks you're not monitoring
Inaccurate product or service descriptions
"[Brand] specializes in [use case they discontinued 2 years ago]"
Impact: Misleads buyers into evaluating you for a use case you don't serve, wasting both their time and your sales resources. Can persist in AI training data for years after the product change.
Outdated pricing information
"[Brand] offers plans starting at $X/month" (your actual pricing is 40% different)
Impact: Creates sticker shock in sales conversations when buyers arrive with AI-given price expectations. Particularly damaging in competitive evaluation scenarios.
Negative review sentiment surfaced in AI framing
"[Brand] has received criticism for customer support response times and complex onboarding"
Impact: Negative review themes become negative AI framing. Affects buyer perception at consideration stage - before your team gets a chance to address objections.
Incorrect category or competitor associations
"[Brand] is a [wrong category] company, competing primarily with [wrong competitors]"
Impact: Positions you in the wrong competitive context, causing buyers to evaluate you against competitors you don't actually compete with.
Regulatory or legal incidents surfacing persistently
"[Brand] faced [regulatory/legal issue from 2022] - worth investigating current status"
Impact: Past incidents persist in AI descriptions long after resolution. Can revive concerns for buyers who haven't encountered the original news.
What ArtificialPulse monitors for brand safety
Accuracy flags
Detects when AI descriptions contain factually incorrect information - wrong pricing, discontinued features, outdated positioning.
Negative framing detection
Classifies each AI mention. Flags when a brand is receiving negative framing, cautionary language, or active discouragement.
Framing change alerts
Notifies when framing shifts negatively - e.g., from positive to hedged or hedged to negative. Catches problems early before they compound.
Cross-platform monitoring
Brand safety issues can manifest differently in ChatGPT, Perplexity, and Google AI Overviews. All three platforms monitored separately.
Competitive framing comparison
Are competitors framed more positively than you in the same response? Relative framing affects buyer preference even when absolute framing is neutral.
Query coverage
Brand safety issues often emerge in specific query contexts (comparison queries, review queries, validation queries) before appearing in broader category queries.
Responding to AI brand safety issues
Inaccurate factual description
Update Wikidata entry. Update Wikipedia article if one exists. Publish clear, authoritative content on your website that can be retrieved by Perplexity. Reach out to publications with incorrect information for corrections.
Outdated pricing in AI
Update your pricing page with clear structured data markup. The issue often comes from old press coverage or articles with specific price mentions. Identify those articles and request updates from publishers.
Negative review sentiment driving negative framing
Address the underlying issues that are generating negative reviews - this is the only path to changing AI framing driven by review sentiment. Then run a review generation campaign on satisfied customers to dilute negative signal.
Old incident surfacing in AI descriptions
Create authoritative current content that contextualizes the incident. Pursue press coverage of your current state. The new, positive signal can eventually dominate older negative signal in AI framing - but it takes time.
Find your AI brand safety risks
Free audit shows your AI framing across platforms - including any inaccurate descriptions, negative framing, or concerning signals.