AI sentiment score
Positive, neutral, negative and mixed framing measured across fixed buyer prompts and engine-specific answer styles.
AI Brand Sentiment Management
The Enough Agency manages how AI systems describe, frame and qualify your brand — with prompt-level sentiment tracking, narrative driver analysis, competitor framing benchmarks, source attribution, hallucination and mispositioning checks, reputation alerts and action plans for ChatGPT, Gemini, Perplexity, Claude, Copilot and Google AI Overviews.
Bring your brand positioning, known reputation risks, competitors, review sources, PR history, support themes and the prompts where buyers compare you. We map how AI describes you today and what needs to shift.
Why Sentiment Needs Management
AI brand sentiment is not just a positive, neutral or negative label. It is the way an answer qualifies the brand: confident or cautious, expert or generic, recommended or merely mentioned, accurate or outdated. Buyers may never click a result if the answer has already framed the brand poorly.
The Enough Agency treats sentiment as narrative intelligence. We identify the prompts where the brand is described weakly, the sources that drive that framing, the competitors benefiting from it and the corrective work needed across content, entity clarity, authority signals and reputation sources.
What We Manage
Positive, neutral, negative and mixed framing measured across fixed buyer prompts and engine-specific answer styles.
Trust, quality, reliability, innovation, value, support and risk themes broken out so the team knows what is driving the tone.
Outdated claims, invented details, wrong category framing and weak differentiation flagged with the answer text that caused them.
The reviews, news, forums, profiles, authority articles and owned pages associated with positive or negative AI framing.
How AI describes you against competitors in comparison prompts, including win/loss framing and preferred-recommendation gaps.
Early warning when negative, cautious or incorrect narratives spike in tracked answers or across source surfaces.
Management Method
Test priority prompts across engines and score sentiment, narrative themes, accuracy, recommendation strength and competitor framing.
Map the prompts, sources, citations, reviews, articles and entity gaps that explain weak or risky AI descriptions.
Update content, structured data, authority signals, review narratives, PR targets and messaging so AI has better evidence to reuse.
Track the next cycles to prove whether sentiment, accuracy, recommendation share and business signals actually moved.
Outputs
A prompt-level view of how ChatGPT, Gemini, Perplexity, Claude, Copilot and AI Overviews describe the brand today.
Trend reporting for sentiment score, recommendation share, hallucination rate, positioning consistency and competitor displacement.
The sources that appear to shape AI sentiment, from owned content and reviews to forums, news, authority articles and competitor pages.
Negative spikes, cautious wording, inaccurate claims, competitor-biased answer patterns and reputation risks recorded with answer evidence.
Prioritized content, PR, authority, review, entity and messaging fixes tied to the prompts and narratives they are meant to improve.
AI referrals, branded search lift, assisted conversions, sales feedback and pipeline influence connected to sentiment movement where data allows.