# B2B Tech Stack Detector — Technographics by Domain (AI) (`fuze/b2b-tech-stack-detector`) Actor

Detect the technology stack behind any B2B domain — CMS, ecommerce, analytics, CRM/marketing, chat, ad pixels, framework, CDN/hosting, payments, email provider — with AI buying-signals and optional fit scoring. A flexible alternative to BuiltWith and Wappalyzer for technographic enrichment.

- **URL**: https://apify.com/fuze/b2b-tech-stack-detector.md
- **Developed by:** [Fredrik Hansson](https://apify.com/fuze) (community)
- **Categories:** Lead generation, AI, Developer tools
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $44.00 / 1,000 domain analyzeds

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?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## B2B Tech Stack Detector — Technographics by Domain (AI)

**Hand it a list of company domains and it tells you exactly what technology each one runs.** This Actor fingerprints every domain's homepage and DNS — CMS, ecommerce platform, analytics, tag managers, CRM and marketing automation, live-chat, ad pixels, JavaScript framework, CDN/hosting, payment provider and email provider — then uses OpenAI to read the stack and tell you **how mature it is** and **whether that company is a fit for what you sell**.

You bring the domains. It hands back a technographic profile per company — the signal most B2B targeting is missing.

![B2B Tech Stack Detector — Technographics by Domain (AI) — input and sample output](https://datatools-hub.vercel.app/actor-shots/b2b-tech-stack-detector.png)

### 🔗 Chain it after your scraper

Already building lead lists with **[Google Maps Scraper (`compass/crawler-google-places`)](https://apify.com/compass/crawler-google-places)**, **[Google Maps Email Extractor (`lukaskrivka/google-maps-with-contact-details`)](https://apify.com/lukaskrivka/google-maps-with-contact-details)** or **[LinkedIn Company Details Scraper (`harvestapi/linkedin-company`)](https://apify.com/harvestapi/linkedin-company)**? Pipe the dataset straight in — paste its dataset ID (via the Integrations tab) and get the full technology stack + buying-signals + fit score per company. Works with any Maps, LinkedIn or company scraper's output.

### Quick start

1. **Fill in the input** — set the fields in the input tab (prefilled with a working example).
2. **Click Start** — the Actor runs and writes one record per item to the dataset.
3. **Get your data** — export to **JSON, CSV or Excel**, or pull it via the API.

***

### The flow

```
1. Paste company domains           2. It fingerprints each one         3. Get a technographic profile
   (domains, URLs or emails    →      homepage headers + cookies    →     technologies · categories ·
    — one per line)                   + HTML + DNS/MX                     email provider · maturity · fit
```

1. **Paste the domains** you want to profile — raw domains, full URLs or even emails (the domain is extracted automatically).
2. **Run the Actor.** Each domain's homepage is fetched (headers and cookies captured even behind a 403/500) and its DNS is checked.
3. **Read the stack.** Every company comes back with the exact technologies detected, the evidence behind each one, its email provider, an AI maturity read and — if you told it what you sell — a fit score.

***

### What you get per domain

For every domain, the Actor:

1. **Fetches the homepage** with a real browser-grade HTTP client that follows redirects and keeps response headers and `Set-Cookie` values **even on 403/500**, so WAFs, hosting and cookie-based tech are still fingerprinted.
2. **Detects the technology stack deterministically** (no AI, no guessing) from ~70 curated signatures across: response headers (`Server`, `X-Powered-By`, `X-Generator`, `X-Vercel-Id`, `CF-Ray`, …), `Set-Cookie` names (`_shopify_*`, `wordpress_*`, `hubspotutk`, `intercom-*`, …), `<meta name="generator">`, script/link hostnames (`cdn.shopify.com`, `js.hs-scripts.com`, `googletagmanager.com`, `static.klaviyo.com`, `snap.licdn.com`, `connect.facebook.net`, …) and inline framework markers (`__NEXT_DATA__`, `ng-version`, `__NUXT__`, …). Each hit carries its **category**, a **confidence** (`high`/`medium`) and the exact **evidence** string that proved it.
3. **Infers the email provider** from the domain's MX records — Google Workspace, Microsoft 365, Zoho, Proofpoint, Mimecast, Fastmail or other.

Then the **AI layer** (OpenAI `gpt-4o-mini`, a fast, low-cost model) reads the detected stack and adds:

- **`techMaturity`** — `legacy` · `standard` · `modern` · `cutting-edge`.
- **`summary`** — one sentence describing their stack.
- **`inferredTeamSignals`** — short buying/team signals grounded in the stack (e.g. *"runs paid acquisition"*, *"data-driven marketing team"*, *"developer-led"*).
- **`fitScore`** (0–100) + **`fitReason`** — only when you set `yourOffering`: how good a prospect this company is for what **you** sell, and why, based on the technologies they actually run. The AI only ever reasons over the detected technologies — it never invents a tool that wasn't found.

***

### Three ways people use it

- **Sales prospecting & ICP targeting** — find companies using Shopify, HubSpot, Klaviyo, Webflow, Intercom or any tool your product plugs into or replaces, and score each domain for fit before a rep ever touches it. Technographic enrichment turns a flat domain list into a prioritised call sheet.
- **Competitive & market intelligence** — a flexible **BuiltWith / Wappalyzer alternative** with no seat-based subscription: map which platforms, analytics suites and ad pixels a whole list of competitors or a market segment runs, and track how modern their stack is.
- **Lead enrichment & routing** — append a technology stack, email provider and maturity signal to every account in your CRM, then route or disqualify leads automatically (e.g. "still on a legacy CMS", "already runs a competing CRM", "runs LinkedIn + Meta pixels → spends on paid").

***

### Input

| Field | Type | Notes |
|---|---|---|
| `domains` | array | **Required.** Company domains, website URLs or emails — one per line. The bare domain is extracted from each. |
| `yourOffering` | string | Optional. What you sell. If set, every domain gets a `fitScore` (0–100) and a `fitReason` based on its detected stack. |
| `maxResults` | integer | Max domains to analyze. One pay-per-event charge per domain analyzed. Default `100`. |
| `maxConcurrency` | integer | How many domains to process in parallel. Default `5`. |
| `model` | string | Override the OpenAI model. Default `gpt-4o-mini`. |

#### Minimal input example

```json
{
  "domains": [
    "shopify.com",
    "notion.so"
  ],
  "yourOffering": "We sell a Shopify app that automates abandoned-cart email flows."
}
```

***

### Output example

One record per domain, pushed to the dataset:

```json
{
  "domain": "shopify.com",
  "website": "https://shopify.com",
  "success": true,
  "status": 200,
  "technologies": [
    { "name": "Cloudflare", "category": "CDN/Hosting", "confidence": "high", "evidence": "header: server=cloudflare" },
    { "name": "Shopify", "category": "Ecommerce", "confidence": "high", "evidence": "script: cdn.shopify.com" },
    { "name": "Google Analytics", "category": "Analytics", "confidence": "high", "evidence": "script: google-analytics.com" }
  ],
  "categories": ["CDN/Hosting", "Ecommerce", "Analytics"],
  "emailProvider": "Google Workspace",
  "techMaturity": "modern",
  "summary": "A modern commerce stack fronted by Cloudflare with Shopify's own platform and Google-based analytics.",
  "inferredTeamSignals": ["ecommerce-native", "data-driven marketing team"],
  "fitScore": 85,
  "fitReason": "Runs Shopify as its core platform, so a Shopify app that automates cart-recovery email is a direct, high-relevance fit."
}
```

When `yourOffering` is omitted, `fitScore` and `fitReason` come back `null`. The deterministic `technologies`, `categories` and `emailProvider` are always populated.

***

### Pay-per-event pricing

This Actor uses **pay-per-event** billing with a single event:

| Event | Charged when |
|---|---|
| `domain-analyzed` | Once per domain successfully analyzed — technology stack, email provider and AI buying-signal summary. |

The deterministic detection layer is effectively free to run; only one small AI call per domain costs anything, and the Actor logs its OpenAI token usage and estimated cost at the end of every run, so the event price always stays well above the model cost.

***

### Setup

The AI layer calls the OpenAI API. Set the environment variable / secret **`OPENAI_API_KEY`** on the Actor. Without it, the full deterministic stack — `technologies`, `categories` and `emailProvider` — still runs and ships; only `techMaturity`, `summary`, `inferredTeamSignals`, `fitScore` and `fitReason` are left empty.

***

### Use it from an AI agent (Apify MCP server)

This Actor is available through the [Apify MCP server](https://mcp.apify.com), so AI agents (Claude, ChatGPT, or anything that speaks MCP) can call it as a tool — hand it a list of `domains` and an optional `yourOffering`, and read back each company's technology stack and fit — with no custom integration code.

***

### Run it locally

```bash
npm install
## put your input in storage/key_value_stores/default/INPUT.json
export OPENAI_API_KEY=sk-...   # optional, enables the AI layer
apify run -p
```

***

### A note on data

This Actor only reads a company's **own public homepage** and its **public DNS/MX records** — the same information any visitor's browser receives. It detects technologies from public fingerprints and never collects personal data.

### Chain it

**This Actor is a building block in the fuze B2B suite.** Wire it to the next step with no code in the **Integrations** tab (trigger: *Run succeeded* → start the next Actor), or run them in sequence.

- ⬆️ Upstream: [B2B Lead List Builder](https://apify.com/fuze/b2b-lead-list-builder) — build the target company list first.
- ⬇️ Next: [B2B Intent Radar](https://apify.com/fuze/b2b-intent-radar) — score buying intent using the detected stack.
- ⬇️ Next: [B2B Competitor Teardown](https://apify.com/fuze/b2b-competitor-teardown) and [DTC Brand Teardown](https://apify.com/fuze/dtc-brand-teardown) — feed the tech stack into a battlecard.

### Related actors

- [Company Enrichment (AI)](https://apify.com/fuze/ai-company-enrichment) — Turn a list of domains into structured company profiles: firmographics, contacts, socials and ICP fit.
- [B2B Intent Radar](https://apify.com/fuze/b2b-intent-radar) — Orchestrates firmographic, technographic and news signals into an AI buying-intent score and outreach angle per company.
- [Google Maps Lead Enricher (AI)](https://apify.com/fuze/gmaps-lead-enricher) — Turns Google Maps scraper output into sales-ready leads with emails, website signals, a lead score and an opener.

# Actor input Schema

## `domains` (type: `array`):

Company domains, website URLs or emails (the domain is extracted). One per line.

## `yourOffering` (type: `string`):

Describe what your company sells. If set, each domain gets a fit score (0-100) and a reason based on its detected stack.

## `datasetId` (type: `string`):

Optional. Read the domains from a previous Actor run's dataset — for no-code chaining via the Integrations tab.

## `datasetField` (type: `string`):

Which dataset field holds the domain/URL. Leave blank to auto-detect (domain, website, companyDomain, url, companyUrl).

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

Max domains to analyze. One pay-per-event charge per domain analyzed.

## `maxConcurrency` (type: `integer`):

How many domains to process in parallel.

## `model` (type: `string`):

Override the model (default gpt-4o-mini).

## Actor input object example

```json
{
  "domains": [
    "shopify.com",
    "notion.so"
  ],
  "maxResults": 100,
  "maxConcurrency": 5
}
```

# Actor output Schema

## `domains` (type: `string`):

All analyzed technology profiles in the default dataset.

# 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 = {
    "domains": [
        "shopify.com",
        "notion.so"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("fuze/b2b-tech-stack-detector").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 = { "domains": [
        "shopify.com",
        "notion.so",
    ] }

# Run the Actor and wait for it to finish
run = client.actor("fuze/b2b-tech-stack-detector").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 '{
  "domains": [
    "shopify.com",
    "notion.so"
  ]
}' |
apify call fuze/b2b-tech-stack-detector --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,fuze/b2b-tech-stack-detector"
        }
    }
}
```

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/g325FeDogD7K9T4sD/builds/PxCbscUUWjXuhcm7V/openapi.json