> ## Documentation Index
> Fetch the complete documentation index at: https://deepline.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# PredictLeads: Company News Events

> List a company's news events by domain (launches, hires, expansions, partnerships), with dates and categories. Includes SDK V2 examples, schemas, and cost.

List a company's news events by domain (launches, hires, expansions, partnerships), with dates and categories.

> [!NOTE]
>
> Tool ID: `predictleads_company_news_events`

## Run this action

Use the TypeScript SDK for one call. Use `ctx.tools.execute(...)` inside a Play for durable calls, scheduled calls, or CSV processing.

```ts theme={"languages":{"custom":["/languages/deepline-prompt.json"]}}
import { Deepline } from 'deepline';

const deepline = await Deepline.connect();
const result = await deepline.tools.execute(
  'predictleads_company_news_events',
  {
    "company_id_or_domain": "example.com"
  },
);

console.log(result.toolResponse.raw);
```

### CLI

```bash theme={"languages":{"custom":["/languages/deepline-prompt.json"]}}
deepline tools execute predictleads_company_news_events --input '{
  "company_id_or_domain": "example.com"
}' --json
```

## Example response

The static output schema lacks the details for a reliable example. Use `deepline describe predictleads_company_news_events --json` for the complete live contract.

## Input reference

Returns a list of company's News Events.

| Name | Type | Required | Default | Details |
| - | - | - | - | - |
| `payload.company_id_or_domain` | `string` | Yes | — | Company's ID or domain. |
| `payload.found_at_from` | `string` | No | — | Only return `NewsEvents` found after given date (ISO 8601). Format: `date`. |
| `payload.found_at_until` | `string` | No | — | Only return `NewsEvents` found before given date (ISO 8601). Format: `date`. |
| `payload.categories` | `array` | No | — | Comma-separated (,) `NewsEvent` categories. |
| `payload.page` | `integer` | No | `1` | Page number of shown items. **NOTE**: If the parameter is not provided, the meta property `count` will be omitted from response for performance reasons. Minimum: 1. |
| `payload.limit` | `integer` | No | `100` | Limit the number of shown items per page. Maximum: 1000. |

> [!NOTE]
>
> This input schema is too large for an inline display. Read the complete live contract with `deepline describe predictleads_company_news_events --json`.

## Output reference

Standard tool result payload.

| Name | Type | Required | Default | Details |
| - | - | - | - | - |
| `result.data` | `object` | Yes | — | Since 2016, PredictLeads has detected over 8 million relevant news signals, which are available for 2 million companies globally. The News Events are sourced from some 19 million blogs, news and PR sites and distilled into relevant categories by machine learning algorithms. The News Events dataset includes fields such as Formatted Signal, Signal Category, Most Relevant Source URL, Article Sentence, Article Body, Article Author, Found At, Effective Date and other News Event information. |
| `result.data.data` | `array` | Yes | — | — |
| `result.data.data[].id` | `string` | Yes | — | ID of the `NewsEvent` object. Format: `uuid`. |
| `result.data.data[].type` | `"news_event"` | Yes | — | Type of the `NewsEvent` object. |
| `result.data.data[].attributes` | `object` | Yes | — | Attributes of the `NewsEvent` object. |
| `result.data.data[].attributes.summary` | `string` | Yes | — | A short, human readable excerpt of this `NewsEvent` data. |
| `result.data.data[].attributes.category` | `"acquires" \| "merges_with" \| "sells_assets_to" \| "loses_client" \| "signs_new_client" \| "declares_bankruptcy" \| "files_suit_against" \| "has_issues_with" \| "closes_offices_in" \| "decreases_headcount_by" \| "attends_event" \| "expands_facilities" \| "expands_offices_in" \| "expands_offices_to" \| "increases_headcount_by" \| "opens_new_location" \| "goes_public" \| "has_earnings" \| "has_revenue" \| "has_valuation" \| "invests_into" \| "invests_into_assets" \| "receives_financing" \| "hires" \| "leaves" \| "promotes" \| "retires_from" \| "integrates_with" \| "is_developing" \| "launches" \| "ends_partnership_with" \| "partners_with" \| "receives_award" \| "recognized_as" \| "identified_as_competitor_of" \| "spins_off_company" \| "spins_off_division"` | Yes | — | Name of the category this `NewsEvent` represents. |
| `result.data.data[].attributes.found_at` | `string` | Yes | — | Date & time (ISO 8601) when the `NewsEvent` was first discovered. As each NewsEvent aggregate the same signal from multiple sources (NewsArticles), this attribute represents the most common or earliest `published_at` date of these NewsArticles. Format: `date-time`. |
| `result.data.data[].attributes.confidence` | `number` | Yes | — | A numerical score between 0 and 1 that represents PredictLeads’ estimated reliability of a news event. This value is always 1 when the event has been manually reviewed and approved by a human analyst. It can also be 1 for certain automatically detected events when the system determines they meet the highest certainty threshold. PredictLeads already balances match rate and accuracy by limiting NewsEvents, so we don’t recommend applying additional filters based on the confidence score. Minimum: 0. See the live schema for the complete constraint. |
| `result.data.data[].attributes.article_sentence` | `string` | Yes | — | Sentence in the article where `NewsEvent` information was found. |
| `result.data.data[].attributes.planning` | `boolean` | Yes | — | When true, the `NewsEvent` is planned to happen. |
| `result.data.data[].attributes.amount` | `string \| null` | Yes | — | This is a string of a value as extracted from text. |
| `result.data.data[].attributes.amount_normalized` | `integer \| null` | Yes | — | Integer value of "amount" property in USD. |
| `result.data.data[].attributes.assets` | `string \| null` | Yes | — | Assets found in the NewsEvent. |
| `result.data.data[].attributes.assets_tags` | `array` | Yes | — | An array of assets tags extracted from "assets" property. |
| `result.data.data[].attributes.award` | `string \| null` | Yes | — | Award type this `NewsEvent` is announcing. |
| `result.data.data[].attributes.contact` | `string \| null` | Yes | — | Person name that the `NewsEvent` mentions. |
| `result.data.data[].attributes.event` | `string \| null` | Yes | — | The name of the event the company attended. |
| `result.data.data[].attributes.effective_date` | `string \| null` | Yes | — | Date (ISO 8601) the `NewsEvent` is mentioning. Format: `date`. |
| `result.data.data[].attributes.division` | `string \| null` | Yes | — | Whether `NewsEvent` concerns a specific division. |
| `result.data.data[].attributes.financing_type` | `string \| null` | Yes | — | A financing type this `NewsEvent` is mentioning. |
| `result.data.data[].attributes.financing_type_normalized` | `"pre_angel" \| "angel_plus" \| "angel_plus_plus" \| "angel" \| "angel_1" \| "angel_2" \| "angel_3" \| "pre_seed" \| "seed_plus" \| "seed_plus_plus" \| "seed" \| "seed_1" \| "seed_2" \| "seed_3" \| "pre_series_a" \| "series_a_plus" \| "series_a_plus_plus" \| "series_a" \| "series_a1" \| "series_a2" \| "series_a3" \| "pre_series_b" \| "series_b_plus" \| "series_b_plus_plus" \| "series_b" \| "series_b1" \| "series_b2" \| "series_b3" \| "pre_series_c" \| "series_c_plus" \| "series_c_plus_plus" \| "series_c" \| "series_c1" \| "series_c2" \| "series_c3" \| "pre_series_d" \| "series_d_plus" \| "series_d_plus_plus" \| "series_d" \| "series_d1" \| "series_d2" \| "series_d3" \| "pre_series_e" \| "series_e_plus" \| "series_e_plus_plus" \| "series_e" \| "series_e1" \| "series_e2" \| "series_e3" \| "pre_series_f" \| "series_f_plus" \| "series_f_plus_plus" \| "series_f" \| "series_f1" \| "series_f2" \| "series_f3" \| "pre_series_g" \| "series_g_plus" \| "series_g_plus_plus" \| "series_g" \| "series_g1" \| "series_g2" \| "series_g3" \| "pre_series_h" \| "series_h_plus" \| "series_h_plus_plus" \| "series_h" \| "series_h1" \| "series_h2" \| "series_h3" \| "pre_series_i" \| "series_i_plus" \| "series_i_plus_plus" \| "series_i" \| "series_i1" \| "series_i2" \| "series_i3" \| "pre_series_j" \| "series_j_plus" \| "series_j_plus_plus" \| "series_j" \| "series_j1" \| "series_j2" \| "series_j3" \| "pre_angel_bridge" \| "angel_plus_bridge" \| "angel_plus_plus_bridge" \| "angel_bridge" \| "angel_1_bridge" \| "angel_2_bridge" \| "angel_3_bridge" \| "pre_seed_bridge" \| "seed_plus_bridge" \| "seed_plus_plus_bridge" \| "seed_bridge" \| "seed_1_bridge" \| "seed_2_bridge" \| "seed_3_bridge" \| "pre_series_a_bridge" \| "series_a_plus_bridge" \| "series_a_plus_plus_bridge" \| "series_a_bridge" \| "series_a1_bridge" \| "series_a2_bridge" \| "series_a3_bridge" \| "pre_series_b_bridge" \| "series_b_plus_bridge" \| "series_b_plus_plus_bridge" \| "series_b_bridge" \| "series_b1_bridge" \| "series_b2_bridge" \| "series_b3_bridge" \| "pre_series_c_bridge" \| "series_c_plus_bridge" \| "series_c_plus_plus_bridge" \| "series_c_bridge" \| "series_c1_bridge" \| "series_c2_bridge" \| "series_c3_bridge" \| "pre_series_d_bridge" \| "series_d_plus_bridge" \| "series_d_plus_plus_bridge" \| "series_d_bridge" \| "series_d1_bridge" \| "series_d2_bridge" \| "series_d3_bridge" \| "pre_series_e_bridge" \| "series_e_plus_bridge" \| "series_e_plus_plus_bridge" \| "series_e_bridge" \| "series_e1_bridge" \| "series_e2_bridge" \| "series_e3_bridge" \| null` | Yes | — | A normalized value of `financing_type` property, where possible. Allowed: `pre_angel`, `angel_plus`, `angel_plus_plus`, `angel`, `angel_1`, `angel_2`, `angel_3`, `pre_seed`, `seed_plus`, `seed_plus_plus`, `seed`, `seed_1`, `seed_2`, `seed_3`, `pre_series_a`, `series_a_plus`, `series_a_plus_plus`, `series_a`, `series_a1`, `series_a2`, `series_a3`, `pre_series_b`, `series_b_plus`, `series_b_plus_plus`, `series_b`, `series_b1`, `series_b2`, `series_b3`, `pre_series_c`, `series_c_plus`, See the live schema for the complete constraint. |
| `result.data.data[].attributes.financing_type_tags` | `array` | Yes | — | An array of financing type categories. |
| `result.data.data[].attributes.headcount` | `integer \| null` | Yes | — | A number of people mentioned organization is involved with. |
| `result.data.data[].attributes.job_title` | `string \| null` | Yes | — | Job title this `NewsEvent` is mentioning. |
| `result.data.data[].attributes.job_title_tags` | `array` | Yes | — | An array of job title tags. |
| `result.data.data[].attributes.location` | `string \| null` | Yes | — | Location of where the `NewsEvent` happened or where the organization has expanded/relocated to. **NOTE**: It is built from the `location_data` attributes if present. Otherwise, it falls back to the original unnormalized detected location string. |
| `result.data.data[].attributes.location_data` | `array` | Yes | — | **NOTE**: Currently contains at most one object. |
| `result.data.data[].attributes.location_data[].city` | `string \| null` | Yes | — | City of the location. |
| `result.data.data[].attributes.location_data[].state` | `string \| null` | Yes | — | State of the location. |
| `result.data.data[].attributes.location_data[].zip_code` | `string \| null` | Yes | — | ZIP code of the location. |
| `result.data.data[].attributes.location_data[].country` | `string \| null` | Yes | — | Country of the location. |
| `result.data.data[].attributes.location_data[].region` | `string \| null` | Yes | — | Region of the location. |
| `result.data.data[].attributes.location_data[].continent` | `string \| null` | Yes | — | Continent of the location. |
| `result.data.data[].attributes.location_data[].fuzzy_match` | `boolean \| null` | Yes | — | `true` indicates that location data may not have been extracted accurately. `false` indicates that location data has been extracted correctly. |
| `result.data.data[].attributes.product` | `string \| null` | Yes | — | Name of the product as mentioned by this NewsEvent. |
| `result.data.data[].attributes.product_data` | `object` | Yes | — | — |
| `result.data.data[].attributes.product_data.full_text` | `string \| null` | Yes | — | Full product name as recognized by Named Entity Recognition, without further cleaning of the product name. |
| `result.data.data[].attributes.product_data.name` | `string \| null` | Yes | — | Cleaned name of the product referenced in the NewsEvent. |
| `result.data.data[].attributes.product_data.release_type` | `string \| null` | Yes | — | Product release type. |
| `result.data.data[].attributes.product_data.release_version` | `string \| null` | Yes | — | Product release version. |
| `result.data.data[].attributes.product_data.fuzzy_match` | `boolean \| null` | Yes | — | When true we might not have extracted the product name cleanly. When false exact product name should be extracted well. |
| `result.data.data[].attributes.product_tags` | `array` | Yes | — | List of tags regarding the product. |
| `result.data.data[].attributes.recognition` | `string \| null` | Yes | — | Name of the recognition the company received. |
| `result.data.data[].attributes.vulnerability` | `string \| null` | Yes | — | Company issue. |
| `result.data.data[].relationships` | `object` | Yes | — | The data contains references to related objects listed in the `included` property. |
| `result.data.data[].relationships.company1` | `object` | No | — | The data contains a reference to related `CompanyLite` object listed in the `included` property. |
| `result.data.data[].relationships.company1.data` | `object` | Yes | — | — |
| `result.data.data[].relationships.company1.data.id` | `string` | Yes | — | ID of the included `CompanyLite` object. Format: `uuid`. |
| `result.data.data[].relationships.company1.data.type` | `"company"` | Yes | — | Type of the included `CompanyLite` object. |

The table shows the first 50 fields. Use the live contract below for every field.

> [!NOTE]
>
> This output schema is too large for an inline display. Read the complete live contract with `deepline describe predictleads_company_news_events --json`.

## Deepline cost

* Pricing model: `fixed` (per call).
* Estimated Deepline credits: `0.56` per pricing unit.

## Related documentation

* [PredictLeads provider guide](https://deepline.com/docs/providers/predictleads/guide.md)
* [SDK V2 quickstart](https://deepline.com/docs/sdk-v2/quickstart.md)
* [SDK reference](https://deepline.com/docs/sdk-v2/sdk-reference.md)
* [Run tools across a CSV](https://deepline.com/docs/sdk-v2/batch-csv.md)


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