# Fashion Intelligence Bundle (`runtime/fashion-intelligence-bundle`) Actor

Find official fashion brand websites, social profiles, event mentions, retail footprint signals, and hiring signals in one scored AI-ready dataset.

- **URL**: https://apify.com/runtime/fashion-intelligence-bundle.md
- **Developed by:** [scraping automation](https://apify.com/runtime) (community)
- **Categories:** Lead generation, Business, AI
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $110.00 / 1,000 fashion intelligence records

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

## Fashion Intelligence Bundle

Find fashion brand websites, social profiles, event mentions, retail signals, and content opportunities.

### Who this is for

- Fashion market researchers
- Retail intelligence teams
- Brand partnership teams

### What it helps you do

- Research fashion brands and events
- Collect official links and social profiles
- Track retail footprint and market signals

### Inputs you can use

- Brand, city, season, or event context
- Source URLs
- Maximum records

### Data you get

- brand
- website
- social links
- event mention
- location signal
- source URL
- confidence score

### How to get better results

- Start with a narrow, specific query or a small list of source URLs.
- Use realistic limits for the first run, then increase the volume once the output looks right.
- Keep source URLs, dates, and location context when you need repeatable market monitoring.
- Review a few sample records before connecting the dataset to a larger workflow.

### Notes

- Results depend on what the public source exposes at run time.
- Some pages may hide, delay, rename, or remove fields, so individual records can have partial data.
- Use the built-in output table to inspect results before exporting to spreadsheets, dashboards, or automation tools.

### Support

If a run returns unexpected data, open an issue from the Actor page with the input used, the run ID, and the result you expected.

# Actor input Schema

## `workflow` (type: `string`):

Business workflow label stored on each output item.

## `brandName` (type: `string`):

Single fashion brand to analyze. Used when Brand names is empty.

## `brandNames` (type: `array`):

Batch brands, one per line. When non-empty, this overrides Brand name.

## `city` (type: `string`):

Optional city context, usually from a fashion event.

## `season` (type: `string`):

Optional season or collection context.

## `eventName` (type: `string`):

Optional event context.

## `country` (type: `string`):

Optional country context.

## `date` (type: `string`):

Optional event, monitoring, or report date in YYYY-MM-DD format.

## `includeBrandResolution` (type: `boolean`):

Find and score likely official brand websites and social profiles inside this bundle Actor.

## `includeWebSignals` (type: `boolean`):

Collect supporting web signals for the selected workflow using direct web search.

## `searchQueries` (type: `array`):

Optional custom queries, one per line. If empty, the Actor generates workflow-specific queries from the brands and context.

## `customQueries` (type: `array`):

Extra queries for official-site resolution, one per line.

## `manualCandidates` (type: `array`):

Optional candidate URLs to score. Add brandName per candidate when running multiple brands.

## `searchProviders` (type: `array`):

Search providers to use. Brave requires BRAVE\_SEARCH\_API\_KEY or braveApiKey.

## `braveApiKey` (type: `string`):

Optional Brave Search API key. Prefer using the BRAVE\_SEARCH\_API\_KEY environment variable.

## `maxBrands` (type: `integer`):

Maximum brands to process.

## `maxResults` (type: `integer`):

Maximum normalized records saved by the bundle.

## `maxCandidatesPerBrand` (type: `integer`):

Maximum official-site candidates scored per brand.

## `maxSearchResults` (type: `integer`):

Maximum raw search/manual candidates before domain deduplication.

## `maxWebSignals` (type: `integer`):

Maximum supporting web signals to save.

## `maxWebQueries` (type: `integer`):

Maximum workflow search queries to execute.

## `acceptThreshold` (type: `integer`):

Confidence required to mark a brand site as accepted.

## `reviewThreshold` (type: `integer`):

Minimum confidence for needs\_review. Lower scores are rejected.

## `includeRejected` (type: `boolean`):

Save rejected official-site candidates. Disabled by default to keep paid datasets focused.

## `crawlHomepage` (type: `boolean`):

Fetch candidate homepages to extract metadata, JSON-LD organization names, and social links.

## `blockedDomains` (type: `array`):

Additional domains to reject before crawl.

## `allowedDomains` (type: `array`):

Domains that bypass the built-in false-positive domain filter.

## `mockMode` (type: `boolean`):

Generate sample fashion signals without network access.

## `failOnNoResults` (type: `boolean`):

Fail the run if no normalized records are produced.

## `requestTimeoutSecs` (type: `integer`):

Timeout for search and homepage crawl requests.

## `useApifyProxy` (type: `boolean`):

Enable Apify Proxy for direct web discovery.

## `proxyConfiguration` (type: `object`):

Advanced Apify Proxy or custom proxy settings.

## Actor input object example

```json
{
  "workflow": "brand-official-site-finder",
  "brandName": "Dior",
  "brandNames": [],
  "city": "Paris",
  "season": "Spring/Summer 2027",
  "eventName": "Paris Fashion Week",
  "country": "France",
  "includeBrandResolution": true,
  "includeWebSignals": true,
  "searchQueries": [],
  "customQueries": [],
  "manualCandidates": [],
  "searchProviders": [
    "duckduckgo"
  ],
  "maxBrands": 10,
  "maxResults": 25,
  "maxCandidatesPerBrand": 5,
  "maxSearchResults": 20,
  "maxWebSignals": 10,
  "maxWebQueries": 10,
  "acceptThreshold": 80,
  "reviewThreshold": 40,
  "includeRejected": false,
  "crawlHomepage": true,
  "blockedDomains": [],
  "allowedDomains": [],
  "mockMode": false,
  "failOnNoResults": true,
  "requestTimeoutSecs": 20,
  "useApifyProxy": false,
  "proxyConfiguration": {}
}
```

# Actor output Schema

## `signalType` (type: `string`):

Normalized fashion signal type

## `workflow` (type: `string`):

Workflow that produced the record

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

Fashion brand associated with the record

## `company` (type: `string`):

Company or brand entity

## `title` (type: `string`):

Record title

## `url` (type: `string`):

Primary URL

## `candidateUrl` (type: `string`):

Candidate URL scored during brand resolution

## `officialUrl` (type: `string`):

Accepted official brand URL when confidence is high enough

## `domain` (type: `string`):

Primary domain

## `description` (type: `string`):

Snippet, homepage description, or source summary

## `socialLinks` (type: `string`):

Detected social links

## `confidence` (type: `string`):

0-100 confidence score

## `confidenceReasons` (type: `string`):

Positive scoring reasons

## `negativeSignals` (type: `string`):

Risk or quality warnings

## `reviewStatus` (type: `string`):

accepted, needs\_review, or rejected

## `location` (type: `string`):

Location context

## `city` (type: `string`):

City context

## `country` (type: `string`):

Country context

## `season` (type: `string`):

Season or collection context

## `eventName` (type: `string`):

Fashion event context

## `date` (type: `string`):

Event, monitoring, or report date

## `source` (type: `string`):

Source module or provider

## `sourceUrl` (type: `string`):

Source or listing URL

## `sourceQuery` (type: `string`):

Search query that produced the record

## `sourcePosition` (type: `string`):

Position in the source listing

## `sourceData` (type: `string`):

Source-specific fields preserved after normalization

## `scrapedAt` (type: `string`):

ISO timestamp when the record was created

# 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 = {
    "brandName": "Dior"
};

// Run the Actor and wait for it to finish
const run = await client.actor("runtime/fashion-intelligence-bundle").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 = { "brandName": "Dior" }

# Run the Actor and wait for it to finish
run = client.actor("runtime/fashion-intelligence-bundle").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 '{
  "brandName": "Dior"
}' |
apify call runtime/fashion-intelligence-bundle --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,runtime/fashion-intelligence-bundle"
        }
    }
}

```

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/RNL08cbYrkarO7aca/builds/g7lAg0Nn1RJosBxZy/openapi.json