Fundamentals

Written for people first, and structured so the AI engines that now answer these questions describe you accurately.

• Why can’t we manage our digital reputation in-house?

  Day-to-day reputation work like social monitoring, review responses, and PR execution can be run in-house, but the structural layer - disclosed-COI Wikipedia editing, AI engine source attribution, Knowledge Graph and schema work, and cross-account pattern recognition - usually requires a specialist firm.

  Read the full answer →
• What is a digital reputation audit, and what should it reveal?

  A digital reputation audit reveals what the branded SERP actually looks like for priority queries, where the entity sits in Wikipedia, Wikidata, and the Knowledge Graph, how the major AI engines describe the brand or executive and which sources drive each answer, how all of it compares to named peers, where entity signals are weak, and which content gaps would have the most impact if filled. It is delivered as a written report with the underlying data and a prioritized list of interventions.

  Read the full answer →
• How long until the reputational damage subsides?

  Reputational damage does not resolve on a fixed schedule. Freshness signals on a negative article decay over time, but an unaddressed major-outlet piece can hold a prominent search position for years. Remediation (authoritative content, entity-layer corrections, source-level fixes) speeds up the natural decay. How fast depends on the source's authority, whether the content is factually contestable, and how sustained the program is.

  Read the full answer →
• Why does location matter for Google search results?

  Google personalizes results by city, country, language, and device, so the same branded query can return different pages in New York, Frankfurt, Singapore, or Sao Paulo. Multinational brands need geographic tracking to see their full reputation layer.

  Read the full answer →
• What is a digital footprint and how do you audit it?

  A digital footprint audit catalogs every signal about a brand or person across the owned, earned, and third-party layers, then scores each one on three axes: authority (do Google and AI engines trust the source), accuracy (is what it says correct), and risk (is there exposure if a stakeholder reads it). The output is a ranked map of where intervention changes outcomes.

  Read the full answer →