AI reads your reputation footprint — your reviews, your mentions, what forums say — and most brands have no idea what it's telling buyers. PACT360 maps that footprint into a single ORM Trust Score, then shows you in dollars what your negative reviews are costing, and what to fix first.
When buyers — and the AI they ask first — judge your brand, the damage shows up in marketing. But it rarely starts there. We find it, price it, and route it to the people who can actually fix it.
The negative reviews shaping what your buyers and AI see.
By how severe each one is, and how widely it's seen.
What those negatives are costing you, in revenue.
Who owns each issue — and which need the executive team, not a campaign.
Negative reviews cluster into root causes — product, distribution, authenticity, service, value, ESG, communications and digital. The rest belong to operations, product, legal and the executive team. We show you where each issue originates, and how high it needs to go.
Your reputation isn't one score per channel. AI blends your marketplace reviews, forum threads and owned reviews into a single answer — so we attribute the cost by channel and govern it centrally, not in silos.
Four research modules, one explainable score. Every figure is traced back to the actual evidence behind it — no black box, no dashboard to manage.
Every platform AI systems read — Reddit, TikTok, Google Reviews, Trustpilot, editorial, Instagram, YouTube — scored and weighted by how heavily each one influences AI recommendations.
We test what ChatGPT, Perplexity, Gemini and Google AI Mode actually say about you, then compare it to what your platforms should earn. The gap between the two is the headline finding.
Every negative review is tagged to one of eight categories, weighted by severity and visibility, and routed to the function that owns it — separating fast, response-driven issues from deep, cross-functional ones.
Your negative reviews, priced. We model the annual revenue they put at risk — so the board sees a cost it can act on, not an abstraction.
Buyers filter, trust and pay based on what other people say about a brand — and the effect is largest exactly where the stakes are highest. The research is consistent and independent.
Figures are drawn from the sources above as published; survey figures (2, 3) are refreshed annually and may shift year to year, so cite the edition. Conversion figures (1) come from controlled analyses of ~5M+ page views and 100k+ reviews; the underlying methods are peer-reviewed in sources 4 and 5.
A 30-second indicative read on your Trust Score, your AI suppression risk, and the revenue your unanswered negative reviews may be costing. A full mapping replaces these estimates with measured figures.
A five-stage research process, run once and re-run on a schedule. Each stage produces evidence the next one builds on.
Deep dives into ORM strategy, AI citation readiness, and brand reputation in the age of AI-mediated discovery.
Reddit is among the most-cited domains by AI systems. A single thread can shape buyer perception before they ever reach your site.
Read More → AI & SearchYour brand may be earning a 75/100 ORM score, but AI systems show only 40/100. Here's why — and how to close the gap.
Read More → StrategyStop talking about reputation as a soft metric. Here's how to price the revenue impact of unanswered reviews in dollars.
Read More → EnterpriseA comprehensive guide to building and maintaining a strong online reputation at scale.
Read More →Begin with a point-in-time analysis you own. When you're ready, move to a continuous operating model that keeps every owner — across functions — accountable.
A full, redacted mapping — the House of Dunmore 360 Trust Index — showing every module, the AI citation gap, the financial model and the action plan, exactly as a client receives them.
Book a 20-minute mapping call. We'll run a live AI-citation snapshot of your brand beforehand — so you arrive with a real finding, not a pitch.
Book a mapping call