What is Five Blocks’ approach to AI and LLM reputation management?

AI engines now shape how stakeholders perceive a brand, alongside search results. Five Blocks handles this with its Track / Analyze / Impact methodology, applied to AI through the AIQ™ platform. The cycle runs continuously: monitor what models say, work out what drives those outputs, then act at the source level to shift them.

1. Track: monitor what AI engines say

AIQ™ polls the eight AI engines it currently tracks daily: ChatGPT, Copilot, Gemini, Google AI Overview, Perplexity, Grok, Claude, and Google AI Mode, using a defined prompt set for each tracked brand or executive. The platform captures the full response, the sources each engine cited, the sentiment, and the narrative themes. Each engine can return a different answer to the same question, so tracking them together shows which version of the brand story is winning and where the gaps are.

2. Analyze: identify which sources drive the narrative

AIQ™ ties each engine’s answer back to its underlying sources, showing which outlets, Wikipedia content, owned pages, or other assets shaped the response. The platform also runs the same prompts for named peers, giving a direct comparison of AI narrative themes, source attribution, sentiment, and prominence across competitors. That comparison shows which sources are gaining weight, which are losing it, and where the biggest opportunities are.

3. Impact: strengthen the source ecosystem

AI engines build answers from their underlying source ecosystem, not from editable model outputs, so the effective intervention is at the source layer. Five Blocks uses the diagnostic from the Analyze phase to decide which sources to strengthen: Wikipedia articles and Wikidata entries, authoritative third-party coverage, owned properties with clean schema markup, and entity infrastructure that helps engines identify and describe the brand correctly. Changes to high-authority sources can reach retrieval-equipped engines within days; training-based updates follow on retraining cycles.

Ongoing: peer benchmarking and narrative tracking

The program does not end at intervention. AIQ™ keeps tracking narrative shifts over time, comparing the client against named peers and flagging any regression or new source that gains outsized weight. Monthly reports show trend lines across the engines AIQ™ tracks, so the engagement runs on data rather than assumption.