# B2B Lead Finder & Decision Maker Qualifier (Laya & JEV AI) (`eternallabs/b2b-lead-finder-decision-maker-qualifier-laya-jev-ai`) Actor

Find verified decision makers (CTOs, CEOs, VP Engineering) matching your Ideal Customer Profile. Multi-dimensional buyer scoring and outreach angles.

- **URL**: https://apify.com/eternallabs/b2b-lead-finder-decision-maker-qualifier-laya-jev-ai.md
- **Developed by:** [Jona](https://apify.com/eternallabs) (community)
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

Pay per usage

This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have.

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

## 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

## AI Prospect Intelligence — B2B Decision Maker Discovery

> **Don't just find leads. Decide which leads matter.**

Find the exact decision-makers you should contact. Powered by **open-source Laya decision intelligence** with optional **Jev** support and deterministic rules fallback.

***

#### Core Question Answered:

**Who should I contact?**

#### Key Capabilities:

- **Company & People Discovery**: Identify verified organizations and discover authentic professional profiles across key functional departments.
- **Multi-Dimensional Decision Ranking**:
  - **Company Fit Score (0-100)**: Evaluates industry alignment, employee size, and tech stack match.
  - **Role Relevance Score (0-100)**: Ranks relevance to your specific service offering.
  - **Buying Intent Score (0-100)**: Analyzes verifiable signals (hiring, infrastructure migrations, expansions).
  - **Decision Maker Score (0-100)**: Determines budget signing and executive authority.
  - **Data Quality Score (0-100)**: Transparent verification of emails, direct phones, and profiles.
- **Actionable Outreach Angles**: Tailored tactical positioning derived from verified empirical evidence.
- **Actor Chaining**: Can directly ingest outputs from **Business Opportunity Finder**.

#### Example Output:

```json
{
  "company_name": "Apex Cloud Systems",
  "person_name": "Sarah Jenkins",
  "role": "VP of Engineering",
  "seniority": "vp",
  "prospect_score": 94,
  "company_fit_score": 96,
  "role_relevance_score": 95,
  "buying_intent_score": 91,
  "decision_maker_score": 93,
  "why_this_prospect": "Sarah leads 45+ engineers and has 6 open cloud infrastructure positions.",
  "recommended_angle": "Cloud infrastructure optimization & engineering capacity",
  "evidence": [
    {
      "source": "careers_portal",
      "evidence": "6 open Kubernetes and Terraform engineering listings",
      "confidence": 0.95
    }
  ]
}
```

# Actor input Schema

## `serviceToSell` (type: `string`):

The product, service, or solution offering you pitch (e.g., 'Cloud Security Audits', 'AI Engineering', 'B2B SEO').

## `targetIndustry` (type: `string`):

Industry sector of target accounts (e.g., 'Fintech', 'SaaS', 'Healthcare', 'E-commerce').

## `targetRoles` (type: `array`):

Titles of target decision makers to identify.

## `targetSeniority` (type: `array`):

Seniority tiers to prioritize.

## `locations` (type: `array`):

Target geographic locations or cities.

## `countries` (type: `array`):

Target countries.

## `companySizeMin` (type: `integer`):

Minimum employee count.

## `companySizeMax` (type: `integer`):

Maximum employee count.

## `targetTechnologies` (type: `array`):

Technologies or software stacks of interest.

## `maxCompanies` (type: `integer`):

Maximum target organizations to discover and inspect.

## `maxProspects` (type: `integer`):

Maximum qualified prospect profiles to return.

## `minimumProspectScore` (type: `integer`):

Discard prospects scoring below this threshold.

## `decisionEngine` (type: `string`):

System-1 decision engine to use: 'laya' (open-source default), 'jev' (hosted API), or 'rules' (deterministic rules).

## `upstreamDatasetIdOrUrl` (type: `string`):

Optionally chain from Business Opportunity Finder or Google Places results by providing Apify dataset ID or URL.

## `seedCompanies` (type: `array`):

Direct list of companies to research and uncover decision-makers for.

## Actor input object example

```json
{
  "serviceToSell": "AI Engineering & Modernization",
  "targetIndustry": "Fintech & SaaS",
  "targetRoles": [
    "Chief Technology Officer",
    "VP Engineering",
    "Head of Product",
    "Founder",
    "Chief Information Officer"
  ],
  "targetSeniority": [
    "c_level",
    "vp",
    "director",
    "founder"
  ],
  "locations": [
    "San Francisco, CA",
    "New York, NY",
    "London, UK"
  ],
  "countries": [
    "United States",
    "United Kingdom"
  ],
  "companySizeMin": 10,
  "companySizeMax": 500,
  "targetTechnologies": [
    "Python",
    "AWS",
    "Kubernetes",
    "PostgreSQL"
  ],
  "maxCompanies": 10,
  "maxProspects": 20,
  "minimumProspectScore": 50,
  "decisionEngine": "laya"
}
```

# Actor output Schema

## `prospects` (type: `string`):

Full dataset of qualified decision makers with contact details, scoring breakdown, and angles.

## `csv` (type: `string`):

Download prospect list formatted for CRM import.

## `summary` (type: `string`):

Key stats on qualified decision-makers, seniorities, and average scores.

# 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 = {};

// Run the Actor and wait for it to finish
const run = await client.actor("eternallabs/b2b-lead-finder-decision-maker-qualifier-laya-jev-ai").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 = {}

# Run the Actor and wait for it to finish
run = client.actor("eternallabs/b2b-lead-finder-decision-maker-qualifier-laya-jev-ai").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 '{}' |
apify call eternallabs/b2b-lead-finder-decision-maker-qualifier-laya-jev-ai --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,eternallabs/b2b-lead-finder-decision-maker-qualifier-laya-jev-ai"
        }
    }
}
```

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/MY2FskwYcqySUz5dg/builds/mecBEfrjwed35jfBy/openapi.json