## New Business Filings Leads Scraper
**💰 $5.00 / 1 000 results** · pay only for results · no credit card to try
*The devil's in the data — we get it to you before your competitors do.* 😈
Pull a daily feed of newly-formed US business entities from official state Secretary-of-State registries (NY, CO, CT, OR) — company name, entity type, formation date, registered agent, and address, filtered by date range. Built for sales and lead-gen teams.
***
### 🎯 What this scrapes
This Actor pulls a **date-range feed of newly-formed US business
entities** straight from official state Secretary-of-State open-data
registries — New York, Colorado, Connecticut, and Oregon in v1. Give
it a `dateFrom` (and optionally a `dateTo`), and it returns one row
per entity that registered within that window, sorted newest-first.
This is **not** a name-search/verification tool — you don't need to
know the company names in advance. It's built for the opposite use
case: **who just formed a company?** That's the raw stream sales,
banking, insurance, payroll, and SaaS onboarding teams want to reach
while the ink is still wet. (Need to verify a *known* company name
instead? See our sibling Actor, `opencorporates-alternative-scraper`.)
### 🔥 What we handle for you
- 🛡️ **Browser fingerprint rotation** — `curl-cffi` impersonates real Chrome / Firefox / Safari TLS handshakes so every request looks like a browser, not Python.
- 🔁 **Retries with exponential backoff** on `408 / 429 / 5xx` — up to 5 attempts per request, `Retry-After` honoured.
- 🧱 **Graceful degradation per state** — if one state's dataset hiccups, that state is skipped with a warning; the rest of the run keeps going.
- 🌐 **Full pagination** — up to 10,000 rows per jurisdiction per run, paginated automatically via `$offset`.
- 🧊 **Clean, typed dataset rows** — Pydantic-validated, ISO-8601 timestamps, a shared cross-state schema despite each state publishing wildly different raw field names.
- 💰 **Pay-Per-Event pricing** — you only pay for rows that land in your dataset. No data, no charge (beyond the small warm-up fee).
### 💡 Use cases
- **Business banking & commercial insurance outreach** — reach brand-new entities before incumbents do.
- **Payroll / benefits / SaaS onboarding vendors** — target companies at the exact moment they need these tools.
- **Marketing agencies** — build fresh, low-competition prospect lists that competitors haven't touched yet.
- **Market research** — track new-entity formation volume and entity-type mix over time, per state.
- **Feed a CRM on a schedule** — pair this with an [Apify Schedule](https://docs.apify.com/platform/schedules) for a rolling daily/weekly new-filings feed.
### ⚙️ How to use it
1. Click **Try for free** at the top of the page.
2. Set **Date from** — the earliest formation date to include. Leave **Date to** blank to default to today (UTC).
3. Optionally narrow **Jurisdictions** to just the states you care about, and set an **Entity types** filter or **Max results per jurisdiction**.
4. Click **Start**. Rows stream into the run's dataset as each jurisdiction is pulled.
5. Export from **Storage → Dataset** as JSON, CSV, or Excel — or fetch via the API.
6. Want this to run automatically? Wire it into an [Apify Schedule](https://docs.apify.com/platform/schedules) with a rolling `dateFrom`/`dateTo` window.
### 📥 Input
| Field | Type | Required | Default | Notes |
|---|---|:--:|---|---|
| `dateFrom` | `string` | **yes** | — | Earliest formation date to include (inclusive), ISO `YYYY-MM-DD`. |
| `dateTo` | `string` | no | today (UTC) | Latest formation date to include (inclusive), ISO `YYYY-MM-DD`. |
| `jurisdictions` | `array` | no | `["NY","CO","CT","OR"]` | Which state registries to pull from. |
| `entityTypes` | `array` | no | none | Optional filter on **raw, per-state** entity-type values — e.g. `"DOMESTIC LIMITED LIABILITY COMPANY"` in NY vs. `"DLLC"` in CO for the same legal form. Not normalized across states. |
| `maxResultsPerJurisdiction` | `integer` | no | `1000` | Cap on rows pulled per jurisdiction per run (1-10,000), paginated via `$offset`. |
| `proxyConfiguration` | `object` | no | `{"useApifyProxy": false}` | Optional — these are public open-data APIs, not known to fingerprint clients. |
#### Example input
```json
{
"dateFrom": "2026-07-01",
"dateTo": "2026-07-19",
"jurisdictions": ["NY", "CO", "CT", "OR"],
"entityTypes": null,
"maxResultsPerJurisdiction": 1000,
"proxyConfiguration": {"useApifyProxy": false}
}
```
### 📤 Output
Every row is one newly-formed entity within `[dateFrom, dateTo]` for one jurisdiction.
| Field | Type | Notes |
|---|---|---|
| `entity_id` | `string` | State's internal id (`dos_id` / `entityid` / `id` / `registry_number`). |
| `entity_name` | `string` | State's on-file entity name. |
| `jurisdiction` | `string` | `NY`, `CO`, `CT`, or `OR`. |
| `entity_type` | `string \| null` | e.g. `DOMESTIC LIMITED LIABILITY COMPANY`, `DLLC` — raw per-state, not normalized. |
| `formation_date` | `string` | ISO `YYYY-MM-DD` — the field this Actor filters/sorts on. |
| `status` | `string \| null` | Raw state status label at scrape time (e.g. `Good Standing`). |
| `principal_address` | `string \| null` | Flattened single-line address. |
| `registered_agent_name` | `string \| null` | When published. |
| `registered_agent_address` | `string \| null` | Flattened single-line address. |
| `source_record_url` | `string` | Direct link to the raw SODA record. |
| `registry_url` | `string` | The state's open-data dataset landing page. |
| `scraped_at` | `string` | ISO 8601 UTC timestamp this row was written. |
#### Example output
```json
{
"entity_id": "7969358",
"entity_name": "URGB LLC",
"jurisdiction": "NY",
"entity_type": "DOMESTIC LIMITED LIABILITY COMPANY",
"formation_date": "2026-07-17",
"status": null,
"principal_address": null,
"registered_agent_name": "URGB LLC",
"registered_agent_address": "32 Jagger Court, Melville, NY 11747",
"source_record_url": "https://data.ny.gov/resource/n9v6-gdp6.json?dos_id=7969358",
"registry_url": "https://data.ny.gov/d/n9v6-gdp6",
"scraped_at": "2026-07-19T12:00:00+00:00"
}
```
### 💰 Pricing
Pay-Per-Event — you pay only when these events fire:
| Event | USD | What it is |
|---|---:|---|
| `actor-start` | $0.005 | One-off warm-up charge per run |
| `result-row` | $0.005 | Per newly-formed entity row written to the dataset |
Example: 1 000 rows ≈ **$5.00**. No subscription, no minimum, no card
to start — Apify gives every new account $5 of free credit. Because
this Actor caps at `maxResultsPerJurisdiction` (default 1,000) per
jurisdiction, a full 4-state run with defaults costs at most ~$20;
narrow the date range or the jurisdiction list to control spend.
### 🚧 Limitations
This is a **v1 with 4 confirmed states** (NY, CO, CT, OR) — not the
broader multi-state coverage some incumbents advertise. We picked
these four because each publishes a free, keyless, machine-readable
Socrata dataset sourced from the state's own Secretary of State;
adding a state is a per-state adapter, not a schema break, so more are
on the roadmap. `entityTypes` filters against each state's own **raw**
vocabulary — it is intentionally not normalized across states (a
domestic LLC is `"DOMESTIC LIMITED LIABILITY COMPANY"` in NY but
`"DLLC"` in CO). This is a one-shot pull, not a push/webhook alert —
run it on a schedule yourself via [Apify Schedules](https://docs.apify.com/platform/schedules)
for a recurring feed. Each run is stateless; deduplication against
prior runs or your own CRM is on you. `status` is nullable because NY
and OR don't publish an explicit status field.
### 🔗 Use with n8n
Wire this Actor into your [n8n](https://n8n.io) automations with the official [Apify node](https://www.npmjs.com/package/@apify/n8n-nodes-apify):
1. Add the **Apify** node and pick the **Run Actor** operation.
2. Set the Actor to `devilscrapes/new-business-filings-leads-scraper` and pass your `dateFrom`/`dateTo` window as JSON.
3. Chain a **Get Dataset Items** step to read the structured rows into the rest of your workflow (Sheets, Slack, your CRM, an AI agent, …).
Because n8n runs on a schedule or trigger, you get a fresh daily
stream of newly-formed companies on autopilot — for example, an
automated Slack digest of every new Colorado LLC formed in the last 24
hours, ready for your SDR team.
### ❓ FAQ
**How is this different from `opencorporates-alternative-scraper`?**
That Actor takes company names you already know and verifies them
against state registries (KYB/compliance use case). This Actor takes
a date range and returns entities you *don't* know yet — the raw
stream of who just formed a company (sales-prospecting use case). Same
4 states, different query shape.
**Why only 4 states in v1?**
Those are the states we've confirmed publish a genuinely free, keyless
Socrata dataset sourced from their own Secretary of State — not a
scrape of a login-gated or Cloudflare-protected search UI. Adding a
5th state is a contained code change, not a rewrite; more states are
on the roadmap.
**Can this notify me automatically when new filings appear?**
Not as a built-in feature in v1 — but it's a perfect fit for [Apify
Schedules](https://docs.apify.com/platform/schedules): set the Actor
to run daily with a rolling 1-2 day `dateFrom`/`dateTo` window and
pipe the dataset into a webhook, Slack, or your CRM.
**Are `entityTypes` values the same across all 4 states?**
No — each state publishes its own vocabulary (e.g. `"DOMESTIC LIMITED
LIABILITY COMPANY"` in NY vs. `"DLLC"` in CO for the same legal form).
`entityTypes` filters against each state's raw values, not a
normalized cross-state enum. Check a few sample rows per state before
building a strict filter.
**How current is the data?**
It's exactly as current as each state's own open-data snapshot. All 4
confirmed jurisdictions returned same-week filings during our most
recent live check, but none of these are real-time transactional
lookups — treat this as "daily-fresh," not "instant."
### 💬 Your feedback
Spotted a bug, hit a weird edge case, or need a 5th state added? Open
an issue on the Actor's **Issues** tab on Apify Console — we ship
fixes weekly and we read every report.
***
Built by **[Devil Scrapes](https://apify.com/DevilScrapes)** 😈 — a small fleet of
opinionated public-data Actors. Honest pricing, real engineering, zero fine print.
# Actor input Schema
## `dateFrom` (type: `string`):
Earliest formation date to include (inclusive), ISO YYYY-MM-DD.
## `dateTo` (type: `string`):
Latest formation date to include (inclusive), ISO YYYY-MM-DD. Leave blank to default to today (UTC) at run time.
## `jurisdictions` (type: `array`):
Which state Secretary-of-State registries to pull newly-formed entities from.
## `entityTypes` (type: `array`):
Optional filter on raw per-state entity-type values (e.g. "DOMESTIC LIMITED LIABILITY COMPANY" in NY vs. "DLLC" in CO) — not normalized across states. Leave empty for no filter.
## `maxResultsPerJurisdiction` (type: `integer`):
Cap on rows pulled per jurisdiction per run, paginated via $offset.
## `proxyConfiguration` (type: `object`):
Optional — state open-data portals are public REST APIs and aren't known to fingerprint clients. Leave off unless you see 403/429.
## Actor input object example
```json
{
"dateFrom": "2026-07-01",
"jurisdictions": [
"NY",
"CO",
"CT",
"OR"
],
"maxResultsPerJurisdiction": 1000,
"proxyConfiguration": {
"useApifyProxy": false
}
}
```
# Actor output Schema
## `datasetItems` (type: `string`):
All dataset items as JSON.
## `datasetItemsCsv` (type: `string`):
Same data exported to CSV.
## `datasetView` (type: `string`):
Open the run dataset in the Console.
# 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 '