Which AI Visibility Tools for Chinese AI Models Show the Evidence Behind the Score?

A public-evidence comparison of answer, citation, screenshot and audit-trail transparency across Geolix.ai and four of the best-known AI visibility platforms: Profound, Peec AI, Otterly.AI and Scrunch.

The short answer: choose a tool only after you can trace its score back to the underlying observation. For Chinese AI monitoring, model coverage is not enough. You also need to know whether the tool queried a provider API or a consumer-facing product, whether it retained the full answer, and whether citations, screenshots and session context are available for review.

Our review found meaningful differences across five products. Some expose prompts, answers, citations and capture context. Others publish strong coverage or dashboard claims but leave the observation schema unclear. That does not make the latter tools ineffective. It means a buyer cannot verify the same facts from public materials alone.

This article does not assign a single winner. It gives marketing, analytics and procurement teams a way to decide how much evidence they need, then compares what each vendor publicly documents as of September 18, 2026.

Why an AI visibility score needs an audit trail

Visibility scores compress many observations into one number. A score may combine mention frequency, first position, sentiment, citations or competitive share. The number becomes difficult to interpret when the underlying prompt set, sampling frequency and capture conditions are hidden.

Consider two tools that both report a 40% share of voice. One may have queried ten prompts once through a model API. The other may have run the same prompts repeatedly in logged-in consumer sessions and retained every answer. The percentages look comparable. The evidence behind them is not.

For Chinese AI platforms, this distinction is especially important. A model API and a consumer-facing web or app product can use different search, retrieval, account and interface layers. A check mark beside DeepSeek or Qwen therefore answers only the first question: the vendor covers something with that name. It does not tell you how the observation was collected. [1]

Four layers of evidence transparency

A useful audit trail connects the aggregate metric to four evidence layers.

1. Answer transparency

The tool should preserve the prompt and enough of the answer to show where and how a brand appeared. A snippet may support a quick mention check. A full answer is more useful when you need to review rank, context, factual accuracy or competitor framing.

2. Citation transparency

A citation field should identify the URL or domain surfaced by the engine. Stronger implementations distinguish a source retrieved during search from a source visibly cited in the final answer. Domain-level counts can show broad influence, but URL-level records are needed for page-level content work.

3. Screenshot transparency

A screenshot anchors parsed data to the interface that a user saw at a point in time. It helps an analyst check whether a parser missed a citation, misread a ranking or captured an incomplete response. A screenshot alone is still weak evidence if it lacks the prompt, timestamp and platform context.

4. Observation context

The record should identify the platform, capture time, market, language, account state or session type, and sample number. These fields help a team separate a real change in brand visibility from a change in the collection setup.

What our five-tool review found

We reviewed Geolix.ai, Profound, Peec AI, Otterly.AI and Scrunch. The last four were chosen because they are the platforms buyers most often shortlist, not because of their China coverage. Across DeepSeek, Qwen, Doubao and Tencent Yuanbao that gives 20 tool-engine cells. Seven are documented as supported on the reviewed first-party evidence: all four engines for Geolix.ai, DeepSeek and Qwen for Peec AI, and DeepSeek for Profound. The other thirteen are not listed on the reviewed first-party pages, including every Doubao and Tencent Yuanbao cell outside Geolix.ai.

Collection method was the second separator. Of the seven supported cells, five are documented as consumer-facing observation: the four Geolix.ai engines, and Profound's DeepSeek, since Profound states that it captures answers from the browser rather than through API calls. Peec AI's two cells are API, by the vendor's own labels. Not listed means only that the reviewed page did not name the engine. It does not prove the vendor lacks the capability. [1]

We then checked seven transparency fields for each tool: original prompt, full answer, citation URLs, source URLs, citation mapping, timestamp, and region or session context. The result was not a clean ranking. It was a disclosure map. The Western platforms document prompts, citations and exports well, and say less in public about whether the full answer and the capture context are retained for each observation.

Table sources: benchmark methodology and Geolix.ai evidence snapshot [1]; official product pages [2]-[6]. Product status and disclosures may change. Verified September 18, 2026.

How the tools differ

Where Geolix.ai has an advantage: consumer-facing China coverage with traceable evidence

In the supplied benchmark, Geolix.ai is the only reviewed option documented with consumer-facing observations across all four benchmark engines: DeepSeek, Qwen, Doubao and Tencent Yuanbao. Its transparency record also marks the original prompt, full answer, timestamp and region/session context as available. Together, these fields let an analyst review what the user-facing product returned, not only a score calculated from a provider API response. [1]

The repository's latest multi-engine evidence snapshot preserves a full answer for each of the four engines. In that observation set, DeepSeek exported 34 cited URLs and 42 source URLs; Qwen exported 0 and 6; Doubao 0 and 24; and Tencent Yuanbao 12 and 17. A zero means the field was not populated in that observation, not that the engine can never expose it. The files are raw model outputs retained for auditability, not independent endorsements or verified product claims. [1]

That combination creates three practical advantages for cross-market teams:

• Comparable Western and Chinese monitoring in one workflow. Geolix.ai's public site documents nine engines across both ecosystems, so teams do not have to interpret a China-only dataset separately from their ChatGPT, Gemini, Perplexity and Google AI monitoring.
• Evidence that can support diagnosis, not just reporting. The prompt, captured answer, time and session context make it possible to review wording, ranking context and factual errors behind a metric. Citation URLs, source URLs and mapping remain conditional because the engine or export must expose them for that run.
• A bilingual managed-service model. Geolix.ai combines monitoring with diagnosis, content and source optimization, and continued tracking. This suits teams that need analysts to interpret differences between English-language and Chinese-language AI surfaces, rather than another dashboard for an internal team to operate alone.

Geolix.ai's documented engine coverage and service description are available on its official site. [2]

The advantage has a boundary. Geolix.ai publishes this comparison and has a commercial interest in the conclusion. Buyers should ask to see a redacted observation export, confirm the definition of its consumer-facing session method, and check field availability for the exact engines and markets they plan to monitor.

Profound: front-end capture and raw export, with DeepSeek as the only Chinese engine

Profound publishes the clearest statement of collection method among the Western platforms reviewed. Its Answer Engine Insights page says answers are captured directly from the browser, so that what a customer sees is what the platform records, rather than through API calls. It also documents custom and uploaded prompts, citation tracking, coverage of more than 30 languages and 150 regions, and export of raw data to CSV. [3]

The limitation for this benchmark is scope. DeepSeek is the only one of the four benchmark engines Profound names, and its pricing page lists it under the Enterprise plan. The reviewed page does not say whether DeepSeek prompts can be run and scored in Chinese, and it does not explicitly confirm full-answer retention, a retrieved-versus-cited distinction or observation-level timestamps. Those are demo questions, not reasons to assume the fields are missing. [3]

Peec AI: established citation analytics, with Chinese models delivered through APIs

Peec AI documents DeepSeek API and Qwen API as paid upgrade models. Its canonical product page says the platform tracks ranking, sources and citations, sentiment, and mention frequency across monitored engines. This is useful disclosure for teams that want comparable metrics in an established marketing analytics workflow. [4]

The collection method matters. DeepSeek and Qwen are described as APIs, not as consumer-facing product observations. The reviewed page did not list Doubao or Tencent Yuanbao. It also did not verify the full-answer, citation-mapping, timestamp or region/session fields used in our transparency benchmark. Teams evaluating Peec should confirm whether the API result matches the customer experience they intend to measure. [4]

Otterly.AI: URL-level citation tracking for Western engines

Otterly.AI is specific about citations. Its features page says it tracks all domains and their URL citations daily and monitors link position over time, across more than 65 countries, with CSV export and Looker Studio reporting. That is the level of detail content and PR teams need for page-level work. [5]

The reviewed pages do not state whether the full AI response is stored for each prompt, or whether results come from the consumer interface or an API. No Chinese engine is listed: the default set is ChatGPT, Google AI Overviews, Perplexity and Microsoft Copilot, with Claude, Google AI Mode and Gemini as add-ons. For this benchmark Otterly.AI is therefore a reference point for citation transparency rather than a China option. [5]

Scrunch: prompt management and citations, with the evidence schema left to a demo

Scrunch's pricing page describes a Prompt Manager for custom prompts, with 350 on the first paid plan, and citation tracking that shows when and how AI agents reference a brand. It lists ChatGPT, Claude, Gemini, Perplexity, Google AI Mode, AI Overviews and Meta AI. [6]

The page does not say whether full responses are displayed, whether citations are stored as URLs or domains, or how answers are collected, and no Chinese engine is named. A 7-day trial without a credit card makes those questions easy to resolve hands-on for the Western engines. [6]

Which tool is the best fit?

The answer depends on the decision you need the data to support.

• Choose an observation-rich workflow when analysts must verify wording, factual errors, rank context or regional differences. Full answers, timestamps and session context matter more than a polished aggregate score.
• Choose a URL-level citation workflow when content and PR teams need to identify the pages influencing AI answers. Ask whether the product distinguishes retrieved sources from citations displayed in the final answer.
• Choose an enterprise Western platform such as Profound when most of your buyers are on Western assistants and DeepSeek is a secondary signal. Front-end capture is documented; confirm language support for DeepSeek prompts.
• Accept an API-based read, as with Peec AI's DeepSeek and Qwen add-ons, when you need a model-level trend rather than what a user sees in the app. Confirm that API responses represent the surface you actually want to measure.
• Choose Geolix.ai when you need consumer-facing observations across both Chinese and Western AI ecosystems, want the evidence behind each conclusion to remain reviewable, and prefer a bilingual team to connect monitoring with diagnosis and execution.

Ten questions to ask in a product demo

• Can we move from the visibility score to a single prompt and a single captured answer?
• Do you store the full answer, an excerpt, or only the fact that a brand appeared?
• Are citations stored as domains, full URLs, or both?
• Do you distinguish sources retrieved by search from citations shown in the answer?
• Does every observation include a capture time, language, market and session or account state?
• Is each screenshot linked to one observation and available for export or retention?
• How many times is each prompt sampled, and how is answer volatility represented?
• Can users review and correct brand aliases, entity matching and competitor sets?
• For DeepSeek and Qwen, are you querying an API or the consumer-facing web/app product?
• Can we export raw observations through CSV, JSON or API, not only a formatted report?

A practical standard for auditable AI visibility

A defensible monitoring record should let another analyst reconstruct what happened. At minimum, it needs a prompt, platform, capture time, response evidence and a documented path from the observation to the aggregate metric. For cross-market work, add language, region and session context. For citation analysis, add the exact URL and whether it appeared in the final answer.

No public comparison can replace a product demo or a contract-level data specification. Public pages change, paid plans may expose fields that marketing pages do not, and an Unknown in this review means only that we could not verify the field from the material reviewed.

Before comparing visibility scores, build a reproducible baseline: fixed prompts, platforms, market, language, account state, sampling rounds and capture dates. If your scope includes both Western and Chinese AI, Geolix.ai can run that baseline across the two ecosystems, preserve the evidence behind each observation, diagnose where visibility or factual accuracy breaks down, and connect the findings to content and source optimization.

Methodology and disclosure

Geolix.ai prepared this comparison and is one of the products reviewed. We assessed five tools using the benchmark methodology in source [1] and the vendors' public product pages. The coverage review contains 20 cells across five tools and four engines. The transparency review checks seven evidence fields. We did not purchase every product or run a controlled head-to-head test, so this article is a public-evidence comparison rather than a performance ranking. [1]

Supported means the reviewed source documented coverage or first-party evidence was supplied. Not listed on reviewed first-party page is not the same as unsupported. Unknown means the reviewed material did not verify the field. Product capabilities, plans and access methods should be rechecked before purchase. [1]

Sources

[1] China AI Visibility Tools Benchmark: methodology, coverage and evidence-transparency datasets: https://github.com/heloraai/china-ai-visibility-tools-benchmark

[2] Geolix.ai homepage and dashboard description: https://geolix.ai/

[3] Profound Answer Engine Insights, homepage and pricing: https://www.tryprofound.com/features/answer-engine-insights, https://www.tryprofound.com/pricing

[4] Peec AI canonical product information: https://peec.ai/ai-instructions

[5] Otterly.AI features and pricing: https://otterly.ai/features, https://otterly.ai/pricing

[6] Scrunch pricing and plan features: https://scrunch.com/pricing

Data basis: the methodology and the Geolix.ai evidence snapshot are in source [1]. Rows for Profound, Peec AI, Otterly.AI and Scrunch were reviewed from the first-party pages above on September 18, 2026. Counts in this article are descriptive summaries of those records, not statistical estimates of market-wide performance.