How do you assess whether a reputation management firm understands AI and LLM search?

Many reputation firms have adopted AI language without the underlying capability. These four signals distinguish firms that genuinely understand the AI engines from those that are improvising.

Signal present

• Proprietary monitoring across leading engines
  You cannot manage what you cannot measure, and credible monitoring has to span every major model. ChatGPT, Gemini, Copilot, Perplexity, Claude, Grok, Google AI Overviews, and Google AI Mode can each return a different answer to the same question about the same entity. A firm watching only one or two is missing most of the picture. Ask which engines are tracked, on what cadence, and what the reporting looks like, then verify that the platform actually produces it.
• Sound influence methodology, source layer, not output editing
  No one edits a model’s output directly. AI narratives are shaped by improving the sources the models draw on: entity signals, authoritative content, structured data, and the broader information environment the engines synthesize. A firm that claims to influence model outputs without explaining the source mechanism is either confused or misrepresenting what it does. The correct framing is always upstream, fix what the models read, and the answers follow.
• Structured narrative reporting over time
  A single screenshot of what ChatGPT says today tells you almost nothing. AI answers vary by phrasing, by model, and by day, and they shift as the underlying sources change. Meaningful reporting characterizes the narrative across all tracked engines on a consistent prompt cadence and shows how it moves over time. That trend line is what makes the monitoring actionable.
• Integration with the broader reputation program
  AI narrative is downstream of the same entity and content work that drives search. A firm that treats AI monitoring as a standalone product, disconnected from Wikipedia, entity signals, and the broader result set, misses that the inputs are shared. The strongest programs tie AI narrative management directly to the search and entity work so that improvements reinforce each other.

Improvising

• Talks about AI but cannot show a monitoring platform or name which engines are tracked
• Claims to influence model outputs directly, without explaining the source mechanism
• Reports AI results as one-off screenshots rather than trended, multi-engine data
• Treats AI as a separate service with no connection to the rest of the reputation work

Five Blocks built AIQ™ specifically to satisfy the first criterion, tracking the eight engines AIQ currently monitors on a consistent cadence and attributing which sources are shaping each answer, and the monitoring ties directly into the entity and content work that shapes those sources over time.