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

AI engines, not just search results, now shape how stakeholders perceive a brand. Five Blocks addresses this through its Track / Analyze / Impact methodology, applied to AI via the AIQ™ platform. The cycle runs continuously: monitor what models say, understand what drives those outputs, then work 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. Because each engine can return a materially different answer to the same question, tracking these engines simultaneously shows which version of the brand story is winning and where gaps exist.

2. Analyze, identify which sources drive the narrative

AIQ™ attributes each engine’s answer to its underlying sources, revealing which outlets, Wikipedia content, owned pages, or other assets are shaping the response. The platform also runs the same prompts for named peers, providing direct comparison of AI narrative themes, source attribution, sentiment, and prominence across competitors. This comparative view clarifies which sources are gaining weight, which are losing it, and where the highest-leverage opportunities lie.

3. Impact, strengthen the source ecosystem

Because AI engines synthesize answers from their underlying source ecosystem rather than from editable model outputs, the effective intervention is at the source layer. Five Blocks uses the diagnostic from the Analyze phase to prioritize 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 correctly identify and describe the brand. Changes to high-authority sources can be reflected in 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™ continues tracking narrative shifts over time, comparing the client against named peers and flagging any regression or new source gaining outsized weight. Monthly reports show trend lines across the engines AIQ™ tracks so the engagement can be steered by data rather than by assumption.