AI Monitoring
Sentiment and reputation control across AI providers
Track how model perception changes by prompt cluster, identify the drivers behind negative shifts, and stabilize your recommendation narrative.
Start 7-day trialSentiment trends by prompt and model
View where sentiment is positive, neutral, or negative and how changes evolve over scheduled runs.
Reputation-risk detection
Flag harmful framing, factual confusion, and unsupported claims before they become persistent model behavior.
Mitigation planning
Translate sentiment movements into content, source, and campaign actions that improve perception quality.
Why model sentiment deserves daily attention
AI sentiment influences shortlist trust
Even when brands are mentioned, negative or uncertain framing can reduce conversion and decision confidence.
Risk accumulates silently
Small sentiment drifts across high-volume prompts can become major positioning problems if not addressed quickly.
Reputation workflow
Measure sentiment baselines
Track sentiment dimensions across providers and prompt groups with stable, comparable reporting windows.
Identify root causes
Link sentiment deterioration to specific claims, source weaknesses, or competitor framing advantages.
Run targeted corrections
Update pages, proof points, and campaign messaging, then validate whether sentiment recovers.
Typical sentiment-management gaps
Metrics without diagnosis
Teams see sentiment scores but cannot connect shifts to concrete correction opportunities.
Provider drift is treated as one trend
Different models can diverge sharply, requiring platform-specific mitigation.
No link to competitor pressure
Negative movement often coincides with competitor narrative gains that remain invisible.
Reporting is too high-level
Leadership gets score snapshots without an action map tied to prompt clusters.
Related solution modules
Prompt Monitoring
Track recommendation share, sentiment shifts, and response quality at prompt level.
Competitor Ranking
Compare against tracked competitors and identify reclaim opportunities.
Content Gaps + Content Engine
Detect high-impact gaps and turn them into blog and campaign outputs.
Brand Source Audit
Map cited sources and fix authority coverage weaknesses.
AI visibility execution stack
Monitoring, ranking, content, shopping, crawler signals, copilot analysis, and reporting in one operational flow.
AI Search Visibility
Measure recommendation share and visibility performance across providers and prompt clusters.
AI Search Monitoring
Track prompts, recommendation share, sentiment, and response accuracy on scheduled runs.
Content Gaps
Detect missing pages and intents that prevent your brand from being recommended.
Competitor Analysis
Compare your position against tracked competitors and identify reclaim opportunities.
Content Generation
Convert prompt and source insights into publish-ready marketing and product-facing content.
Blog Generation on Autopilot
Generate high-intent blog plans and drafts aligned to recommendation behavior changes.
Shopping Intelligence
Monitor AI shopping exposure, pricing narratives, and recommendation presence on product queries.
Data Copilot Chat
Ask plain-language questions on your AI visibility data and get structured answers fast.
Report Generator
Deliver recurring leadership-ready reports with trend summaries and prioritized next actions.
Crawler Monitoring
Monitor AI crawler behavior and improve model-facing indexing pathways.
Hallucination Control
Validate responses across models and detect hallucinations before they affect customer-facing decisions.
Protect brand trust before sentiment drifts compound
Track model perception continuously and execute reputation corrections with measurable impact.
Activate sentiment monitoring