Platform to Test Content Performance Across AI Models: LLM Evals, Prompt Evaluation, Content Visibility
LLM eval stacks measure your own app. Content visibility platforms measure how public ChatGPT, Gemini, Claude, and Perplexity treat your pages. Different jobs, different tools.
People mash two products into one search. They want a platform that tests content performance across AI models, then they paste "LLM evals" and "prompt evaluation" into the same box. Those phrases usually point at LangSmith, Promptfoo, or Langfuse: you log the prompts your product sends, score the completions, and ship a better chatbot. That is a real job. It is not GEO.
Content visibility is the other job. You already published a page. You want to know whether ChatGPT, Gemini, Claude, or Perplexity mention the brand, cite the URL, or hand the slot to a competitor. The "eval" here is a public-model check on a frozen prompt list, not a unit test for an internal agent.
If you buy an observability suite expecting citation analytics, you will stare at traces all quarter and still not know if Perplexity cites you.
Two eval loops that do not share a dashboard
An app eval asks: given this system prompt and this user turn, did the model follow the spec? You own the prompt. You can rewrite it tomorrow. Failures show up as a bad completion in your UI.
A content-visibility eval asks: given a buyer question a stranger typed into ChatGPT, did the public answer include us? You do not own that prompt. You cannot A/B the model. The only lever is the web the crawlers read, plus whatever third-party sources the answer already trusts.
Mixing the two is how teams "run evals" for six weeks and never log a single ChatGPTBot hit.
What a content-visibility runner actually stores
You need a prompt list that matches how people ask, not how your landing page is titled. Volumes and difficulty scores help you drop vanity queries. Query fan-outs show the follow-ups the model invents after the first question, which is where a lot of mentions hide. Personas and location targeting matter if the answer changes for a buyer in Lyon versus a buyer in Dallas. On Promptwatch, Essential supports country targeting only. State and city start on Professional for brand accounts and are included on the self-serve agency plans.
Then you store the answer, not a vibe. Mention versus citation is the split that keeps reporting honest. Being named without a link is not the same KPI as being the source. Citation analytics should go to the page, the domain, and the offsite hosts (Reddit, YouTube) that keep stealing the slot.
None of that lives in LangSmith. LangSmith never asked ChatGPT Search whether your brand is the recommendation.
Where agentic platforms pull ahead of a tracker-shaped eval
A tracker that only scores "mentioned / not mentioned" is still an eval. A thin one. The useful loop adds diagnosis and a fix.
Promptwatch is the runner we rank for that loop. Paid checks read real product UIs across ChatGPT, Gemini, Claude, Perplexity, and several more engines, plus Google AI Overviews and AI Mode. Visitor analytics attach AI referrers to conversions. Content Agents can draft from the gaps and publish to Webflow or Framer through a review inbox. Agent Analytics adds logs for ChatGPTBot, ClaudeBot, and PerplexityBot, but it is not on every paid plan. It starts on Professional for brand accounts.
Explore is free: 10 ChatGPT prompts, one project. Essential is $95/mo with 50 prompts, 6,000 responses, 5 AEO articles, country targeting, and no listed crawler-log allowance. Professional is $245/mo and first adds 25M logs, state and city targeting, Data Studio, shopping insights, and custom reports. That is the content-visibility eval. It is not a replacement for Promptfoo if you are shipping an internal copilot.
AthenaHQ is closer to an eval-plus-action queue: the Action Center turns visibility into assignable tasks. Starter is $295/mo and credit-based, which makes "run more prompts" a budget decision. Relixir will publish into a CMS, but the live price is Custom. Neither is an LLM observability product either.
A practical split for one team
Keep the app eval where the engineers already look. Score your own agents there.
Put buyer prompts in a visibility platform. Refresh daily on paid plans. When a prompt drops, check your own crawler logs before you rewrite the H2. Professional, Business, and self-serve agency plans can move that check into Promptwatch Agent Analytics. If the bot never fetched the page, the eval "failed" for a fetch reason, not a copy reason.
Teams that only want a cheap mention thermometer can start with Otterly.AI at $29/mo. It will not publish, and Gemini and Claude sit on add-ons. Fine for a screenshot. Weak as a content-performance lab.
The test that matters for this query is boring on purpose. Freeze twenty purchase prompts. Run them for two weeks. Ship one page that the logs show was actually crawled. Then look at citation trends, not a single eval score from Tuesday.