The reputation surface AI engines actually train on.

Why reputation now matters more than ever

AI answer engines are trained on the open web — reviews, social media, news, public forums. When a buyer asks ChatGPT or Perplexity about your category, the engine surfaces what the public web has said about you. A two-year-old Glassdoor cluster of negative reviews shows up in AI summaries the same way a current product launch does. The engines don’t weight by recency the way a buyer would. Your reputation has gone from a fact about you to an active part of every sales conversation.

Compounding this: in early 2026 the FTC finalized rules against AI-generated review fraud, and most enforcement actions name not the agency that wrote the fake review but the brand that benefited. The right answer is not to game the surface — it’s to operate it correctly, transparently, and continuously.

What we monitor

• Review platforms: Trustpilot, Google Business, Yelp, BBB, G2 (for SaaS), Capterra, ConsumerAffairs.
• Employer-rating platforms: Glassdoor, Indeed, Comparably, Built In. We watch these because AI engines absolutely cite them when asked about company culture or buying-decision risk.
• Social and community: Reddit (subreddit by relevance), X / Twitter, LinkedIn brand and people pages, niche forums (Hacker News, IndieHackers, industry-specific Slacks where indexable).
• News and press: Industry trade press, local press, regulatory filings, court records, SEC EDGAR, FOIA records.
• Peer-review and citation: Academic citations, .edu mentions, Wikipedia inbound links, reference lists in research and industry reports.

What we do with what we find

• Triage by severity. Active misinformation gets a same-day workflow. Negative-but-fair feedback gets a slower, FTC-compliant response process. Praise gets amplified to other surfaces.
• Response drafting. We draft owner-responses to reviews in your voice, within FTC guidelines (no compensated reviews, no paid suppression, full disclosure where required).
• Content corrections. Where outdated information appears in news, Wikipedia, or third-party databases, we initiate the correction process via legitimate channels.
• Earned-coverage offsets. Negative coverage doesn’t disappear — it gets offset by stronger positive coverage in higher-authority sources, which AI engines weight more.

What we do not do

We do not buy fake reviews. We do not pressure unhappy customers to delete their feedback. We do not threaten review platforms with legal action over factually-supportable reviews. We do not generate AI reviews and post them under invented identities. The FTC enforcement actions of 2026 made this category of activity meaningfully riskier than the upside it once provided. The market we serve cannot afford it.