What does a reputation management engagement actually involve?

A reputation management engagement is built around a set of interlocking components whose mix and emphasis vary by client, but whose spine stays consistent across every engagement.

Upfront diagnostic

Before any intervention begins, the engagement opens with a baseline audit: what the search results look like today, what AI engines say about the client, where entity signals are weak or missing, and what the competitive or reputational landscape requires.

Written strategy

The diagnostic feeds a written strategy document that prioritizes interventions and sets twelve-month goals against measurable KPIs. This becomes the governing document for the engagement.

Content production

Original content is created across owned properties (the client’s own site and channels) and earned channels (media coverage, contributed articles, third-party profiles) to build the source layer that search and AI engines read from.

Entity optimization

The client’s entity infrastructure is audited and strengthened: Wikidata entry accuracy, Organization and Person schema markup with sameAs links connecting the entity across authoritative profiles, and Knowledge Panel health. A strong, coherent entity signal is the foundation that search and AI engines use to identify and describe the client correctly.

Wikipedia engagement

Where the client has or should have a Wikipedia article, Five Blocks works through Wikipedia’s disclosed conflict-of-interest (COI) process, proposing changes on the Talk page with reliable secondary sourcing, with independent editors reviewing and applying any accepted edits. Undisclosed paid editing violates Wikipedia’s terms of use and is never used. Wikipedia is among the strongest disambiguation signals search engines use and is heavily weighted by AI engines including ChatGPT, Gemini, Perplexity, and Copilot.

AI narrative work

Using AIQ, Five Blocks identifies what eight major AI engines: ChatGPT, Copilot, Gemini, AI Overviews, Perplexity, Grok, Claude, and Google AI Mode, currently say about the client and traces those outputs back to their source-level drivers. Interventions target the underlying sources rather than the AI outputs directly, since AI model outputs cannot be edited or manipulated directly.

Ongoing monitoring

Continuous tracking runs across IMPACT (search result positions and SERP features across keywords, locations, and languages), AIQ (AI engine narrative polling across the eight tracked engines), and WikiAlerts (Wikipedia live-edit notifications for the client’s article). Together these tools catch changes as they happen rather than in a periodic snapshot.

Monthly reporting

A structured written report is delivered each month, tying progress back to the agreed objectives with the underlying data available. Reports connect each metric to the interventions that drove it, making the causal chain visible to the client.