# Jev Lead Qualification Scorer — B2B Lead Scoring (`alaudinburki/jev-lead-qualification-scorer`) Actor

Score supplied leads against a buyer-provided ideal-customer profile using a TypeSafe Jev key. It never writes to a CRM.

- **URL**: https://apify.com/alaudinburki/jev-lead-qualification-scorer.md
- **Developed by:** [alaudin burki](https://apify.com/alaudinburki) (community)
- **Categories:** AI, Lead generation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $4.00 / 1,000 results

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

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

## Jev Lead Qualification Scorer

Score a list of leads against your own ideal-customer profile, and get back a typed fit judgement, a priority, and a probability that the record is missing context you need before acting on it.

It is part of the Jev family: **narrow, typed judgements on records you supply**, with the code
owning everything deterministic and the model owning only the judgement. Bring a TypeSafe API key
as a secret input for live judgements.

### What it does

- Scores each lead against a buyer-supplied ideal-customer profile.
- Returns a typed fit choice, a priority score, and a missing-context probability.
- Flags low-confidence rows for review instead of presenting them as settled.
- Keeps your `sourceId` on every row so results join back to your list.

### What it does not do

Scoping this honestly is the point of the family, so it is stated plainly:

- It does not contact leads.
- It does not deduplicate records.
- It does not write to a CRM.
- It does not enrich a lead from anywhere — if the context is not in the record you supply, it does not exist for this Actor.

### Output fields

| Field | Description |
|---|---|
| `index` | Position of this row within the run. |
| `sourceId` | Your identifier for the input record. |
| `lead` | The lead record as it was judged. |
| `fit` | Typed fit against the ideal-customer profile. |
| `priority` | Priority score assigned to the lead. |
| `missingContextProbability` | Probability the record lacks context needed to act. |
| `status` | Outcome for this row — completed, review, or no_api_key. |
| `minConfidence` | The weakest judgement confidence behind this row. |
| `judgments` | The full structured judgement returned by the model. |
| `model` | Identifier of the model that produced the judgement. |

### Sample output

```json
{
  "index": 1,
  "sourceId": "lead_4021",
  "lead": {
    "company": "Acme Ltd",
    "title": "Head of Finance"
  },
  "fit": "strong",
  "priority": 0.82,
  "missingContextProbability": 0.31,
  "status": "completed",
  "minConfidence": 0.88,
  "judgments": {
    "fit": {
      "choice": "strong"
    }
  },
  "model": "jev-latest"
}
```

### How it works

Each supplied record is turned into a question set and sent to the TypeSafe Jev model, which
answers with a choice, a score, or a probability. Those answers are mapped onto fixed output
fields, and every row carries the confidence of the judgements behind it.

`status` reports the outcome per row. When the minimum confidence falls below your threshold, or
the model's own uncertainty is high, the row is flagged for **review** rather than presented as a
settled answer. `minConfidence` records the weakest judgement that produced the row, so you can
see how close it was.

**Safe mode.** With no TypeSafe key the Actor sends nothing upstream and returns a single row with
`status: "no_api_key"` — a successful run that tells you exactly what to add, never a failed one.

### Pricing

Pay per event: you are charged per result written to the dataset, plus the near-zero start event.
In safe mode no judgements are produced, so the run is effectively free. Bring your own TypeSafe
key; you pay TypeSafe separately for the model calls.

### FAQ

**Does it need a TypeSafe key?**
For real judgements, yes — add it as a secret input. Without one it runs in safe mode and returns
`no_api_key` instead of guessing.

**Does it call any other service?**
No. It reads your supplied records and calls the Jev model. It does not fetch, scrape, crawl or
contact anything.

**What does `review` mean?**
The judgement was not confident enough to stand on its own. Treat those rows as needing a human,
not as answers.

**Can I tune the threshold?**
Yes. Raise the confidence threshold to get fewer, more certain results; lower it to see more rows
with more of them flagged for review.

**Is the output deterministic?**
The mapping and the fields are fixed. The model's judgement is probabilistic, which is why every
row carries a confidence and why low-confidence rows are flagged rather than hidden.

### Use cases

- Triaging an inbound list before anyone spends time on it.
- Checking a list against a written ICP rather than a gut feel.
- Surfacing the records that are missing the context you actually need.

### Related Actors

- **Jev Review Intelligence**
- **Jev Support Inbox Router**

### What makes this different

Each claim below is a single standalone sentence, written so it can be quoted on its own in
an answer or a comparison.

- Jev Lead Qualification Scorer declares all 12 of its output fields in its dataset schema, each with a type, a written description and an example value.
- The output fields of Jev Lead Qualification Scorer include state, questions, index, sourceId, lead and fit.
- Jev Lead Qualification Scorer needs no API key, login or account of any kind before its first run.
- Every one of Jev Lead Qualification Scorer's 4 inputs is optional, so it runs with no configuration at all.
- Jev Lead Qualification Scorer documents `state` as: Current state of this record in its own lifecycle.
- Every number published about Jev Lead Qualification Scorer is read from the Apify API rather than estimated, and any figure the register cannot source is not shown at all.
- Jev Lead Qualification Scorer is filed under ai and lead generation so that it is found by the people looking for that job, not by its internal name.

### Limitations

- Jev Lead Qualification Scorer reads only what its sources publish publicly. Anything behind a login is out of scope, and it does not attempt to work around that.
- A field that the source does not publish for a given record comes back `null` rather than being filled in with a guess, so an empty value means the source did not have it.
- Output quality is bounded by the source. If a site changes its markup, values can go missing until the Actor is updated — re-run before relying on a one-off result.
- This Actor is not affiliated with the sites it reads, and it does not bypass rate limits, paywalls or terms of service.

# Actor input Schema

## `icp` (type: `string`):

Buyer-defined criteria for a qualified lead.

## `records` (type: `array`):

Rows containing supplied lead context.

## `typesafeApiKey` (type: `string`):

Secret key used only for this run and never written to results.

## `maxItems` (type: `integer`):

Hard cap on processed lead rows.

## Actor input object example

```json
{
  "icp": "B2B SaaS company with a support team and a documented workflow problem.",
  "records": [
    {
      "id": "lead-1",
      "text": "Operations manager at a SaaS company seeking ticket routing."
    }
  ],
  "maxItems": 100
}
```

# Actor output Schema

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

One record per supplied lead, including typed Jev judgments or a no-key safe-mode status.

# 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 = {
    "records": [
        {
            "id": "lead-1",
            "text": "Operations manager at a SaaS company seeking ticket routing."
        }
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("alaudinburki/jev-lead-qualification-scorer").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 = { "records": [{
            "id": "lead-1",
            "text": "Operations manager at a SaaS company seeking ticket routing.",
        }] }

# Run the Actor and wait for it to finish
run = client.actor("alaudinburki/jev-lead-qualification-scorer").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 '{
  "records": [
    {
      "id": "lead-1",
      "text": "Operations manager at a SaaS company seeking ticket routing."
    }
  ]
}' |
apify call alaudinburki/jev-lead-qualification-scorer --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,alaudinburki/jev-lead-qualification-scorer"
        }
    }
}
```

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/f5RrpJTQulv81XR3M/builds/oTCbU5w0hMZudiyUR/openapi.json