AthenaHQ vs Scrunch vs Standing

Your CEO sends a screenshot showing ChatGPT failing to list your product when asked for recommendations in your category. You open a browser tab, run the prompt yourself, and your brand appears second. You run it a third time in a private window, and it disappears again. Testing prompts manually produces conflicting answers, which leads most marketing leads to start evaluating AI visibility tracking platforms.

AthenaHQ, Scrunch, and Standing all address the problem of tracking how language models talk about your brand. The functional differences come down to workflow design and measurement methodology.

Where AthenaHQ and Scrunch Excel

AthenaHQ is built around content workflows and prompt management. If your primary goal is to generate copy suggestions, manage team workflows, and brainstorm content variations alongside tracking, AthenaHQ offers a feature set tailored to content teams. Their interface makes it straightforward for writers to take tracked queries and immediately draft material aimed at addressing content gaps.

Scrunch offers strong competitive monitoring and category discovery tools. If you are operating across wide consumer verticals and need high-level dashboard summaries that compare multi-brand portfolios, Scrunch provides broad categorical visualizations. It allows marketing managers to report high-level brand presence alongside traditional social and digital discovery metrics.

Both platforms provide polished executive reporting interfaces. If your leadership team wants clean, high-level dashboards showing broad presence signals and workflow assignments, both AthenaHQ and Scrunch deliver strong software experiences.

Pricing and Tier Structure

As of March 2026, pricing across these three platforms reflects different tracking models.

AthenaHQ published $265 to $295 per month when we last checked on 2026-08-10, covering keyword and prompt monitoring, scaling up to enterprise tiers based on user seats and custom integrations. Check their pricing page before acting on that figure: it changes.

Scrunch published around $300 per month when we last checked on 2026-08-10, with higher tier custom plans depending on the number of tracked competitors and brand entities. The same caveat applies: verify it against their own page.

Standing uses fixed published pricing tiers:

• Track: $100 per month for 3 domains.
• Optimize: $300 per month for 5 domains with custom prompt basket support.
• Agency: $500 per month for 50 domains with a monthly automated re-scan.

Standing tracks across five specific engines: ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews.

Measurement Method and Sample Variance

The fundamental difference between Standing and both AthenaHQ and Scrunch is how data is collected from non-deterministic engines.

Large language models do not work like traditional search engines. A traditional search engine returns the exact same indexed results for a query run multiple times from the same location. An AI engine generates text probabilistically. If you execute the same prompt five times across five separate browser sessions, the engine may cite your brand twice and omit it three times.

AthenaHQ and Scrunch execute a given prompt once per scan cycle. When a platform runs a prompt once, a result where your brand appears registers as present, and a result where it is absent registers as missing. Running a single prompt query converts a probabilistic distribution into a binary outcome.

Because of this single-run approach, neither AthenaHQ nor Scrunch publishes a confidence interval alongside their metrics. You can verify this directly in their user documentation and reporting interfaces: visibility scores are displayed as static integer scores without margin of error bounds. If a platform reports a single visibility number without an interval, you have no way of knowing whether that figure reflects your baseline or a temporary sampling anomaly.

Standing runs every prompt in a fixed prompt basket five times per question per engine. It calculates the resulting visibility score using a Wilson confidence interval. A score in Standing is never reported as an isolated integer. It is always reported with its interval, such as 34, plus or minus 6.

This statistical bound is necessary because an apparent score movement from 30, plus or minus 6, to 36, plus or minus 6, shares overlapping confidence bounds, whereas bare numbers without intervals create the illusion of trend changes out of random model noise.

Technical Realities and Crawler Behavior

When brands find they are omitted from AI engine answers, marketing teams often assume they have been blocked by search crawlers or that their technical setup is flawed. Vendors sometimes exploit this confusion by selling technical add-ons or promising guaranteed placements.

It is critical to distinguish between training crawlers and answer crawlers.

Training crawlers collect web data to train future foundation models. Blocking a training crawler affects whether your site content enters future model training runs. Answer crawlers fetch live content from the web to answer a user prompt in real time today. Blocking an answer crawler stops the engine from searching and citing your site right now.

The primary training crawlers are:

• GPTBot
• ClaudeBot
• Google-Extended

The primary answer crawlers are:

• ChatGPT-User
• Claude-SearchBot
• OAI-SearchBot
• Perplexity-User
• PerplexityBot
• Claude-User

In Standing's crawler index update on 2026-08-08, we scanned a panel of 54,082 domains, of which 33,670 returned a readable robots.txt file. Out of those 33,670 readable domains, 5,497 blocked at least one AI crawler, but only 2,576 blocked at least one answer crawler.

Looking at specific agent block rates across those 33,670 readable domains:

• GPTBot (training) was blocked by 5,080 domains.
• ClaudeBot (training) was blocked by 4,603 domains.
• Google-Extended (training) was blocked by 4,275 domains.
• PerplexityBot (search) was blocked by 2,147 domains.
• ChatGPT-User (search) was blocked by 2,074 domains.
• OAI-SearchBot (search) was blocked by 1,579 domains.
• Perplexity-User (search) was blocked by 1,402 domains.
• Claude-SearchBot (search) was blocked by 1,400 domains.
• Claude-User (search) was blocked by 1,384 domains.
• Googlebot (other) was blocked by 610 domains.

In our census of active Y Combinator companies on 2026-08-07, we evaluated 4,226 active companies. Of those, 3,755 had readable robots.txt files, and 253 blocked at least one AI crawler.

Blocking an answer crawler like ChatGPT-User or Perplexity-User actively prevents those systems from reading your site during live search queries. However, simply unblocking these crawlers does not guarantee that an engine will cite your brand.

Never buy software that promises or guarantees a citation, mention, or ranking. Large language models are non-deterministic, and no third-party tool controls their output. Furthermore, do not buy tools or services on the premise that adding schema markup or maintaining an llms.txt file will increase your citations. There is no reliable evidence that schema markup or llms.txt files improve brand inclusion in AI engine outputs.

Choosing the Right Platform

Selecting between these tools comes down to what your team actually needs to execute:

• Choose AthenaHQ if your main priority is writing workflows, generating content suggestions, and providing collaborative prompt management for content writers.
• Choose Scrunch if you require broad competitive benchmarking across multi-brand consumer portfolios and want high-level visual summaries for executive stakeholders.
• Choose Standing if you need statistically rigorous brand tracking across the five major engines, want to isolate sampling noise using confidence intervals, and want fixed pricing without custom enterprise sales overhead.