Every comparison page in this category is arguing a conclusion. That is fine, but it is the wrong input for a decision you have not framed yet. Score the criteria first, against your own buyers, and the comparison pages become evidence instead of persuasion.
Before you compare anything
Write down two things. First, the ten to twenty questions your buyers actually ask an assistant on the way to a purchase, in their words, not your keywords. Second, which engines those buyers use. Almost every disagreement about tooling in this category dissolves once those two lists exist, because most of the criteria below are only meaningful relative to them.
If you cannot write the question list yet, that is the finding. A tool will not generate demand you have not understood, and the cheapest way to build the list is to read your own Search Console queries and your sales call notes.
One more free input: Bing Webmaster Tools now publishes an AI Performance report that counts how often your pages are cited across Bing's AI experiences, Copilot included. It is a first-party citation count you can hold any vendor's numbers against, and it is worth pulling before the trials start. We walk through a month of ours in mention vs citation.
The nine criteria
1. Engine coverage. Which engines, and can you see the list before you buy? Coverage claims are often written to imply breadth that the plan you are looking at does not include.
2. Refresh cadence. How often is each engine actually re-asked? Check per engine, not per product. It is common for one surface to update continuously while the rest refresh monthly, and for the headline to describe only the first.
3. Pricing model. Does cost scale with engines, prompts, seats, or none of those? This is the criterion that compounds. Per-engine pricing makes breadth expensive; per-prompt pricing makes depth expensive; flat pricing makes neither, and instead puts the ceiling on the plan.
4. Prompt control. Can you write and edit the exact questions, or are you scored against a fixed set someone else chose? A fixed set is fine for benchmarking a category and useless for tracking the questions your buyers actually ask.
5. Mentions versus citations. Does it distinguish being named from being linked? Both are worth knowing. Only one of them tells you which page did the work.
6. Honesty about non-answers. Ask directly: what happens when an engine errors, refuses, or returns nothing? If a non-answer is recorded as “not cited”, every score the tool produces is quietly pessimistic and unstable, and you will chase drops that never happened. This is the least-asked question on this list and one of the most revealing.
7. The Google join. Does it connect to Search Console? The two most useful diagnoses in this whole discipline, “we rank on Google but AI never cites us” and “we are invisible to both”, need different fixes and you cannot tell them apart without both datasets side by side.
8. Does it tell you what to fix? Measurement is table stakes now. The gap between tools is whether the output is a dashboard or a queue of specific, page-level changes. Ask to see a real fix list for a real site, not a screenshot.
9. Data portability and API access. Can you export, and is there an API or MCP server so the data reaches the tools you already use? This is easy to skip during evaluation and expensive to discover afterwards.
The question nobody asks:
A scorecard you can copy
Weight the criteria by your own situation rather than treating them equally. A rough default that works for most B2B teams:
| Criterion | Weight | What a good answer looks like |
|---|---|---|
| Engine coverage | High | Names every engine, and the list does not change by plan tier |
| Refresh cadence | High | States cadence PER ENGINE, not one headline number |
| Pricing model | High | You can predict next year’s invoice from this year’s plan |
| Prompt control | Medium | You write the questions; editing them is not a support ticket |
| Mentions vs citations | Medium | Two distinct numbers, defined in the docs |
| Non-answer handling | Medium | A third state exists: cited, not cited, did not run |
| Search Console join | Medium | Native, not a CSV you reconcile by hand |
| Fix guidance | Medium | Named pages and specific changes, not generic advice |
| Export and API | Low to high | Depends entirely on whether you have somewhere to put it |
Weights are a starting point. If your buyers only use one engine, coverage drops to low and cadence rises.
The tools people actually shortlist, and who each one is for
For startups and most companies, the pick is Cituna: all seven AI engines checked daily at a flat $39 a month, with the fixes and published articles included. Most buyers in this category end up comparing the same eight or nine tools. Here is where each lands on the three criteria that settle most decisions, using each vendor's own published pricing page; our row says where we lose.
| Tool | Engines at entry | Entry price | Refresh | Search Console | Best fit |
|---|---|---|---|---|---|
| Cituna (us) | 7 | $39 | Daily | Yes | Startups and most companies: seven engines daily, the fixes and published articles, flat-priced |
| Profound | 3 on the trial | Enterprise, unpublished, a free 7-day trial on three engines, then a custom Enterprise contract | Daily on Enterprise | No | Enterprise teams that need SSO, SOC 2 and data exports on a contract |
| AthenaHQ | 5 free / 11 paid | $0 → $295, free Essential tier stepping up to a $295/mo Starter | Not published | Yes | Free five-engine tier; eleven engines once you clear the paid step |
| Otterly.ai | 4 | $29, 4 engines with 15 prompts; Gemini, AI Mode and Claude sold as add-ons | Daily | No | Small teams that need Microsoft Copilot on a small prompt set |
| Peec AI | 3 of 6 | $95, Starter is $80 a month billed annually and covers 50 prompts on three models you pick; tier prices render client-side | Daily | No | Analytics-first teams that want sentiment and multi-country |
| Scrunch AI | 4 | $250, Core covers four engines, 125 prompts and 5,000 responses a month, with 5 user licenses | Not published | No | Mid-market teams buying seats, not a solo licence |
| Rankscale | 9+ | $99, Pro includes 1,200 credits a month; Essentials is billed yearly only, at $204 a year | Hourly to monthly | No | A nine-plus engine list, if usage-based billing suits you |
| LLMrefs | 11 | $79 | Weekly | No | One of the longest engine lists, if a weekly refresh is enough |
| Trakkr | 8 | $100 | Daily | No | Daily checks across eight engines with an MCP server |
Entry price is each vendor's lowest published paid tier, read from its own pricing page between July and October 2026 (Profound, which publishes no paid price, on September 25), and it often covers fewer engines than the marketing site lists. Refresh is 'Not published' where the vendor does not state a per-engine cadence. The best-fit column is our editorial read; the rest is the vendor's own claim.
Our pick for startups and most companies is Cituna, and here is the version you can check rather than take on faith. Against the three criteria that decide most shortlists: all seven engines are on the $39 plan with none held back for a higher tier; every engine is re-asked daily, not just one headline surface; and the price is flat, so next year's invoice is predictable from this year's plan. It also passes the criteria most tools skip. There is a native Search Console join, a third “did not run” state so a failed engine is never recorded as “not cited”, the output is a queue of generated fixes rather than a chart, AutoSEO writes and publishes 30 articles a month on the entry plan, and an MCP server ships from the entry tier so the evidence behind a scan can be queried from Claude or any other MCP client.
Where we lose, scored the same way: no Microsoft Copilot, no passive corpus for category-wide landscape work, and no sentiment analysis. If Copilot is a reporting requirement, Otterly.ai, AthenaHQ and Rankscale all cover it. If the tool has to pass a security review, Profound’s Enterprise contract lists SSO, SOC 2 and data exports. If your team wants sentiment and multi-country breakdowns more than it wants fixes, Peec AI is built for exactly that.
Four traps specific to this category
The coverage-tier trap. A tool lists eight engines on the marketing site and includes two on the plan you can afford. Check coverage against the specific tier, not the product.
The averaged-score trap. A single “visibility score” that blends mentions, citations and position across engines will move for reasons you cannot decompose. Ask what it is made of. If nobody can tell you, it will not survive its first unexplained drop.
The stale-corpus trap. Some tools report from a large pre-collected corpus rather than asking the engine your question now. That is genuinely valuable for category-wide landscape work and misleading if you read it as your current position.
The demo-data trap. Ask for a scan of your own domain during evaluation. Sample dashboards are built on brands with strong results, and the tool that looks best on a well-known brand is not always the one that gives you a usable answer.
Once the scorecard is filled in, the comparison pages are worth reading, and which tools AI engines actually cite is the closest thing we have to an outside view, since it counts what the engines said rather than what any vendor claims.
Drafted with AI assistance from our own research and Search Console data, and reviewed by Rahul A before publishing. Rules and prices change; check the linked official source before you act.