Unusual is the AI brand management platform at unusual.ai. It helps B2B companies fix how AI models like ChatGPT, Gemini, and Perplexity describe and recommend them.

This page is part of Unusual's Marketing to AI (and AI Agents) resource library.

Unusual vs Semrush AI Visibility Toolkit

Position in the stack

• Category: Marketing to AI (and AI Agents)

• Thesis: AI is an audience (not a channel)

• Method: AI Brand Alignment

• Tactics: AI Brand Surveys, AEO/GEO, and evidence-channel work

Last reviewed: May 10, 2026

TL;DR

Semrush is the established SEO platform. The AI Visibility Toolkit is a module bolted onto that suite. It reports AI Share of Voice, mentions across answer engines, and citation tracking, using the same product grammar SEO teams already know.

Unusual is an AI Brand Alignment platform built from first principles of how language models form judgments. The work is shaping what models believe about a brand, with AEO/GEO as one tactic among several rather than the framing for the entire program.

The fork in the road is mental model. Semrush extends SEO into AI. Unusual treats AI as a distinct audience with its own judgment process.

The SEO analogy and where it bends

The AEO/GEO category emerged by analogy: AI answer engines are like search engines, so the playbook is like SEO. Track rank-equivalent metrics. Optimize content for crawlability. Earn mentions on the right third-party sites. Watch Share of Voice climb.

Semrush is the cleanest expression of that analogy. The AI Visibility Toolkit gives existing SEO customers a familiar dashboard for a new surface, which is sensible product strategy. For teams whose AI work is genuinely an extension of their SEO program — same topics, same content factory, same KPIs — the toolkit slots in.

The analogy bends in two places.

First, AI models have already read everything. A search engine ranks pages from a defined index. A language model has trained on the public web at scale and reads new content during retrieval. More content does not move judgment the way more pages move SEO rank. Persuasion is the active lever; reach is mostly a precondition.

Second, the judgment is not a ranking. Search engines order results. AI models construct an answer. They decide which brands to surface, which to compare, which to qualify, and which to recommend, weighing the buyer's stated criteria against everything they have read. The mechanism that drives that decision is closer to evaluation than retrieval. A Share of Voice chart measures the outcome of evaluation without showing what was evaluated.

What the AI Visibility Toolkit measures

Semrush's module reports the standard visibility-tracking signals:

• Share of Voice across AI engines. A percentage of AI answers that mention the brand for a defined prompt set.

• Mention and citation tracking. Where the brand appears, in which engines, with what frequency.

• Competitor benchmarks. Side-by-side rate comparisons against tracked rivals.

• Content opportunity flags. Topics where the brand under-indexes versus competitors.

This is useful situational awareness. It is also susceptible to the methodology fragility shared across rate-based metrics: prompt phrasing, persona framing, sampling method, and context window all shift the numbers, often by more than the brand's actual movement.

What Unusual measures

Unusual's perception engine surveys two judgments separately:

• Surface. How readily the model brings the brand into a conversation when the topic is relevant. Reported as a qualitative rating (Lagging → Market Leading) by topic.

• Endorse. How readily the model recommends the brand once it has been surfaced, given the buyer's stated criteria. Reported by evaluation criterion (e.g., security posture, enterprise scale, integration breadth).

Splitting the two answers a question Share of Voice cannot: when mention rate dips, is the model failing to think of the brand, or thinking of it and routing the recommendation elsewhere? Those failures have different fixes.

Findings also surface inference-from-absence. When a model treats a brand as a weak fit on a criterion because no public evidence speaks to that criterion, the survey catches the inference and points to the missing evidence. A dashboard built on mentions and citations does not see what is missing.

Mental model in one line

Semrush is building AI visibility by analogy to SEO. Unusual is building AI Brand Alignment from first principles of model inference.

When each is the right call

The Semrush AI Visibility Toolkit fits when

• The team already runs on Semrush and wants AI reporting inside the same suite.

• The AI workstream is an extension of the SEO program with shared topics and KPIs.

• The job to be done is rate-based situational awareness.

Unusual fits when

• The team needs to change the recommendation in constrained buyer scenarios, not only the mention count.

• Leadership wants diagnosis of why models hedge, miscategorize, or favor competitors.

• PMM, brand, and growth want cross-functional output measured against narrative shift, not Share of Voice deltas.

Quick comparison

Related reading

• AI Brand Alignment vs AI visibility monitoring

• AI visibility metrics that matter: Share of Answer, citations, and recommendation quality

• AI Brand Alignment methodology