# Chinese AI Visibility Dataset Analyzer (`zinin/chinese-ai-visibility-dataset-analyzer`) Actor

Normalize existing Kimi, GLM, DeepSeek, Qwen, ChatGPT, Gemini, Perplexity and Google AI Overview datasets; score brand mentions, rank and evidence quality without calling an AI model.

- **URL**: https://apify.com/zinin/chinese-ai-visibility-dataset-analyzer.md
- **Developed by:** [Tim Zinin](https://apify.com/zinin) (community)
- **Categories:** AI, SEO tools
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $4.25 / 1,000 normalized ai observations

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## Chinese AI Visibility Dataset Analyzer

Turn AI-answer rows you already own into one comparable visibility Dataset. The
Actor deterministically normalizes supported ChatGPT, Gemini, Perplexity, Google
AI Overviews, Kimi, Qwen, DeepSeek, and GLM-shaped observations; finds your brand
and configured competitors; records their order; and classifies citation quality.

It does **not** browse, call an LLM, or launch another Actor. You bring the
observations; this Actor makes them consistent, traceable, and ready for analysis.

![Four AI-answer streams becoming normalized visibility records](https://api.apify.com/v2/key-value-stores/2MZFlGXned0z8053B/records/store-normalization.png)

### What you get

Each compatible input observation becomes one `aiObservation 1.0` row in the
default Dataset, with:

- a deterministic brand mention and one-based position among the tracked names;
- detected competitors in first-mention order;
- a normalized model family and model ID;
- structured citation URLs, domains, and a grounding classification;
- source Dataset, Run, row, adapter, timestamp, and bounded evidence provenance;
- quality flags for conditions such as missing citations, fallback timestamps,
  or ambiguous aliases.

The default Dataset contains successful results only. Duplicates, incompatible
rows, source-read errors, and coverage counters go to free Key-value store
records instead of being mixed with analysis-ready data.

This is useful for SEO and brand teams consolidating exports from multiple AI
answer collectors, agencies preparing auditable client reporting, and analysts
who need a stable schema before loading observations into Sheets, a warehouse,
or a dashboard.

### Quick start with the public sample Task

Run the public Task `analyze-chinese-ai-visibility-sample` (Task ID
`IQB5feJ4eXgPyjSkl`) to see the full output without granting access to a private
Dataset. Its four inline rows are fictional, clearly labeled examples; they are
**not live answers or current search evidence**. The unchanged sample produces
four normalized rows.

For your own small sample, replace the inline observations:

```json
{
  "brand": "Acme Running",
  "brandAliases": ["Acme"],
  "competitors": [
    { "name": "StrideLab", "aliases": [] }
  ],
  "inlineRows": [
    {
      "sourceActorId": "apify/chatgpt-search-scraper",
      "query": "Which running shoe brands should beginners compare?",
      "text": "Acme Running and StrideLab are options to compare.",
      "sources": [
        { "url": "https://example.com/running-guide" }
      ],
      "checkedAt": "2026-08-01T10:00:00.000Z"
    }
  ],
  "market": "United States",
  "language": "English",
  "maxRows": 100
}
```

The citation URL in this payload is deliberately illustrative. Supply only
observations and URLs you own or are authorized to process.

### Analyze your Apify Datasets

Choose up to 10 Datasets in **Source Dataset IDs**. The Actor receives scoped
`READ` access only to the resources you select. Then provide one adapter name in
**Dataset adapter hints** for each Dataset, in the same order:

```json
{
  "brand": "Acme Running",
  "competitors": [
    { "name": "StrideLab", "aliases": ["Stride Lab"] }
  ],
  "sourceDatasetIds": ["A1b2C3d4E5f6G7h8I"],
  "sourceDatasetHints": ["apify/gemini-search-scraper"],
  "market": "China",
  "language": "English",
  "maxRows": 500
}
```

The positional hint is mandatory for selected Datasets. Similar ChatGPT and
Gemini result shapes cannot otherwise be mapped reliably. To analyze an Actor
run, select its default Dataset rather than entering a Run ID. Run provenance is
read from that Dataset's metadata when it is available.

Supported adapter hints are:

| Source shape | Adapter hint |
| --- | --- |
| Bulk LLM Runner | `fayoussef/bulk-llm-runner` |
| ChatGPT Search Scraper | `apify/chatgpt-search-scraper` |
| Google AI Overviews Scraper | `apify/google-ai-overviews-scraper` |
| Perplexity Search Scraper | `apify/perplexity-search-scraper` |
| Gemini Search Scraper | `apify/gemini-search-scraper` |
| LLM Brand Visibility | `zinin/llm-brand-visibility` |
| AI Overview Tracker | `zinin/ai-overview-tracker` |
| Already normalized | `ai-observation-1.0` |

For inline JSON or CSV, leave `sourceActorHint` as `auto` or set one global
adapter. A per-row `sourceActorId` takes precedence for inline JSON.

### Input reference

| Field | Required | Purpose |
| --- | --- | --- |
| `brand` | Yes | Canonical tracked brand, up to 100 characters. |
| `brandAliases` | No | Alternative spellings or local-language names. |
| `competitors` | No | Up to 20 competitors, each with optional aliases. |
| `inlineRows` | One source | Up to 1,000 JSON observation rows. |
| `inlineCsv` | One source | Header plus records; JSON-looking cells are decoded. |
| `sourceDatasetIds` | One source | Up to 10 buyer-selected Datasets with scoped `READ`. |
| `sourceDatasetHints` | With Datasets | One positional adapter per selected Dataset. |
| `sourceActorHint` | No | Adapter for inline data; defaults to `auto`. |
| `market`, `language` | No | Fallbacks only when a source row omits them. |
| `maxRows` | No | Hard cap across all sources, from 1 to 1,000. |

You may combine inline and Dataset sources. Processing stops at `maxRows` across
the combined source stream.

### How scoring works

Matching is deterministic and case-insensitive. The Actor searches the supplied
answer for the canonical brand, its aliases, and configured competitors. Brand
position is its one-based order among those tracked names, not a general search
ranking. If the brand is absent, `mentioned` is `false` and `position` is `null`.

Aliases shared by the brand and a competitor are flagged as ambiguous and are
not double-counted. Citations are accepted only from structured source fields;
a URL merely written in answer text is not promoted to evidence. Rows are
deduplicated by deterministic observation provenance.

The Actor does not judge whether an answer is factually correct, positive, or
negative. `groundingStatus` describes the supplied evidence structure, not the
truth of the underlying answer.

### Output Dataset

The default Dataset exposes a table view for brand, query, model, mention,
position, competitors, grounding, quality flags, citations, answer excerpt, and
timestamp. Every complete record also includes all 25 contract fields:

```json
{
  "schemaVersion": "1.0",
  "observationId": "c2f2...",
  "brand": "Acme Running",
  "competitor": "StrideLab",
  "competitors": ["StrideLab"],
  "query": "Which running shoe brands should beginners compare?",
  "modelFamily": "ChatGPT",
  "modelId": "chatgpt-search",
  "market": "United States",
  "language": "English",
  "mentioned": true,
  "position": 1,
  "answerSnippet": "Acme Running and StrideLab are options to compare.",
  "citedUrls": ["https://example.com/running-guide"],
  "citedDomains": ["example.com"],
  "groundingStatus": "grounded",
  "qualityFlags": [],
  "checkedAt": "2026-08-01T10:00:00.000Z",
  "sourceActorId": "apify/chatgpt-search-scraper",
  "sourceRunId": null,
  "sourceDatasetId": null,
  "sourceRowIndex": 0,
  "dependencyUsage": null,
  "dependencyCostUsd": null,
  "evidence": {
    "adapter": "apify/chatgpt-search-scraper"
  }
}
```

`answerSnippet` is capped at 800 characters. Dependency usage and cost are
preserved source metadata when present; they are not this Actor's operating cost.

Three free Key-value store records support auditing:

- `OUTPUT` — source, delivery, billing, and quarantine counts;
- `COVERAGE` — counts by source and normalized model family;
- `QUARANTINE` — bounded reasons for withheld rows, without copying raw payloads.

### Pricing

This Actor uses Pay Per Event. A run charges one automatic Actor-start event and
one `result-found` event for each normalized row atomically delivered to the
default Dataset. Quarantine, duplicates, source errors, and KVS diagnostics do
not emit `result-found` and are free.

![Validated rows enter paid output while quarantined rows remain free](https://api.apify.com/v2/key-value-stores/2MZFlGXned0z8053B/records/store-paid-output.png)

| Tier | Actor start | Each delivered result | Discount |
| --- | ---: | ---: | ---: |
| Free | $0.005000 | $0.005000 | 0% |
| Bronze | $0.004750 | $0.004750 | 5% |
| Silver | $0.004500 | $0.004500 | 10% |
| Gold | $0.004250 | $0.004250 | 15% |
| Platinum | $0.004100 | $0.004100 | 18% |
| Diamond | $0.004000 | $0.004000 | 20% |

Cost is `start price + delivered rows × result price`. At the Free tier, the
unchanged four-row public sample costs at most **$0.025**. Set an Apify charge
limit appropriate to your chosen `maxRows`; the theoretical Free-tier event
maximum at 1,000 delivered rows is $5.005.

### Automation

Start the public Task through the Apify API:

```bash
curl -X POST \
  "https://api.apify.com/v2/actor-tasks/IQB5feJ4eXgPyjSkl/runs?token=YOUR_APIFY_TOKEN&waitForFinish=600"
```

Check that the returned Run status is `SUCCEEDED`, then read its default Dataset
URL. Do not automatically retry a timed-out request until you have checked
whether the original paid Run was created.

In Make or n8n, use an HTTP Request step for the same Task endpoint, wait for a
terminal Run status, and pass the default Dataset items URL to your next step.
For recurring production work, duplicate the Task and replace the illustrative
inline rows with your authorized Dataset selection or pipeline payload.

### Security, privacy, and source rights

- The Actor runs with limited permissions and requests `READ` only for Datasets
  explicitly selected through the Apify resource picker.
- It does not accept provider API keys, tokens, cookies, or arbitrary Run IDs.
- It does not call external AI or search services and does not start child Actors.
- Quarantine and source-error records contain bounded diagnostics rather than raw
  source payloads, and error text is sanitized before storage.
- You are responsible for having the right to process the supplied observations,
  citations, and selected Datasets.

### Limitations

- This is a normalizer and scorer, not a collector. It cannot fetch current AI
  answers or fill an empty input.
- Mention matching is deterministic text matching, not entity resolution,
  sentiment analysis, factual verification, or semantic relevance scoring.
- Only structured source citations count toward grounding.
- Up to 1,000 source rows are processed per Run. Answer processing is bounded to
  5,000 characters, and output snippets are capped at 800 characters.
- If the source omits a timestamp, processing time is used and a quality flag is
  added. Copied Datasets may not expose original Run metadata.
- The Actor creates one normalized snapshot; trend and historical aggregation
  belong in your downstream dashboard or warehouse.

### Troubleshooting

**Why did I get no Dataset rows?**

Open `QUARANTINE` and `OUTPUT`. Common causes are an unsupported row shape, an
incorrect adapter hint, missing usable answer text, or duplicate observations.

**Why was Dataset access denied?**

Select the Dataset through the input resource picker under the same Apify account
or organization that can read it. Pasting a private Run ID is intentionally not
supported.

**Why does `sourceDatasetHints` fail validation?**

Supply exactly one supported adapter string for each selected Dataset, in the
same array order. Do not use `auto` for selected Datasets.

**Why is a URL not listed as a citation?**

The source adapter must expose it in a structured citation field. URLs embedded
only in answer prose are not inferred as evidence.

**How can I verify what was billed?**

Compare the default Dataset item count with `OUTPUT.counts.delivered` and
`OUTPUT.counts.billed`. On-platform they are designed to match; quarantined and
failed source rows remain outside the paid Dataset.

# Actor input Schema

## `brand` (type: `string`):

Canonical brand name used for deterministic mention and position scoring.

## `brandAliases` (type: `array`):

Alternative spellings, transliterations or local-language names. Ambiguous aliases shared with competitors are flagged and not double-counted.

## `competitors` (type: `array`):

JSON array such as \[{"name":"Adidas","aliases":\["Adidas AG"]}]. Position is computed only among the tracked brand and these competitors.

## `inlineRows` (type: `array`):

Rows from a supported collector or aiObservation 1.0. Per-row sourceActorId overrides Source actor hint.

## `inlineCsv` (type: `string`):

Header row plus up to 1,000 records. JSON-looking cells such as sources arrays are decoded.

## `sourceDatasetIds` (type: `array`):

Select buyer-accessible Apify Datasets. READ scope is granted only for these selected resources; source Run provenance is read from Dataset metadata when available.

## `sourceDatasetHints` (type: `array`):

Required when Datasets are selected: one source adapter per Dataset in the same order. This keeps similar ChatGPT and Gemini row shapes mapped to the correct model family.

## `sourceActorHint` (type: `string`):

Optional global adapter hint for inline JSON or CSV rows. Dataset adapters are supplied positionally above. Auto-detection remains the inline default.

## `market` (type: `string`):

Used only when a source row has no market value.

## `language` (type: `string`):

Used only when a source row has no language value.

## `maxRows` (type: `integer`):

Hard cap across every source before normalization and deduplication.

## Actor input object example

```json
{
  "brand": "Nike",
  "brandAliases": [
    "Nike, Inc."
  ],
  "competitors": [
    {
      "name": "Adidas",
      "aliases": [
        "Adidas AG"
      ]
    },
    {
      "name": "New Balance",
      "aliases": []
    }
  ],
  "inlineRows": [
    {
      "sourceActorId": "apify/chatgpt-search-scraper",
      "query": "Which sustainable running shoe brands should a US buyer compare?",
      "text": "Nike, Adidas and New Balance are common options to compare.",
      "sources": [
        {
          "title": "Running guide",
          "url": "https://example.com/running-guide"
        }
      ]
    }
  ],
  "sourceActorHint": "auto",
  "market": "United States",
  "language": "English",
  "maxRows": 100
}
```

# Actor output Schema

## `results` (type: `string`):

API URL for successful aiObservation 1.0 rows. Item count equals result-found count.

## `OUTPUT` (type: `string`):

Counts, billing and source provenance.

## `QUARANTINE` (type: `string`):

Bounded reasons for incompatible, duplicate and withheld rows; raw source payloads are not copied.

## `COVERAGE` (type: `string`):

Counts by source and model family.

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "brand": "Nike",
    "brandAliases": [
        "Nike, Inc."
    ],
    "competitors": [
        {
            "name": "Adidas",
            "aliases": [
                "Adidas AG"
            ]
        },
        {
            "name": "New Balance",
            "aliases": []
        }
    ],
    "inlineRows": [
        {
            "sourceActorId": "apify/chatgpt-search-scraper",
            "query": "Which sustainable running shoe brands should a US buyer compare?",
            "text": "Nike, Adidas and New Balance are common options to compare.",
            "sources": [
                {
                    "title": "Running guide",
                    "url": "https://example.com/running-guide"
                }
            ]
        }
    ],
    "market": "United States",
    "language": "English",
    "maxRows": 100
};

// Run the Actor and wait for it to finish
const run = await client.actor("zinin/chinese-ai-visibility-dataset-analyzer").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "brand": "Nike",
    "brandAliases": ["Nike, Inc."],
    "competitors": [
        {
            "name": "Adidas",
            "aliases": ["Adidas AG"],
        },
        {
            "name": "New Balance",
            "aliases": [],
        },
    ],
    "inlineRows": [{
            "sourceActorId": "apify/chatgpt-search-scraper",
            "query": "Which sustainable running shoe brands should a US buyer compare?",
            "text": "Nike, Adidas and New Balance are common options to compare.",
            "sources": [{
                    "title": "Running guide",
                    "url": "https://example.com/running-guide",
                }],
        }],
    "market": "United States",
    "language": "English",
    "maxRows": 100,
}

# Run the Actor and wait for it to finish
run = client.actor("zinin/chinese-ai-visibility-dataset-analyzer").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "brand": "Nike",
  "brandAliases": [
    "Nike, Inc."
  ],
  "competitors": [
    {
      "name": "Adidas",
      "aliases": [
        "Adidas AG"
      ]
    },
    {
      "name": "New Balance",
      "aliases": []
    }
  ],
  "inlineRows": [
    {
      "sourceActorId": "apify/chatgpt-search-scraper",
      "query": "Which sustainable running shoe brands should a US buyer compare?",
      "text": "Nike, Adidas and New Balance are common options to compare.",
      "sources": [
        {
          "title": "Running guide",
          "url": "https://example.com/running-guide"
        }
      ]
    }
  ],
  "market": "United States",
  "language": "English",
  "maxRows": 100
}' |
apify call zinin/chinese-ai-visibility-dataset-analyzer --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,zinin/chinese-ai-visibility-dataset-analyzer"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/88f1hDbqf26E4hh6X/builds/dXzU57zkdug7NZPCA/openapi.json