DEV Community: NexGenData

New: GLEIF Legal Entity Monitor — Newly Registered LEIs Worldwide — catch freshly registered legal entities worldwide

NexGenData — Fri, 05 Jun 2026 16:17:49 +0000

What it does

GLEIF Legal Entity Monitor watches the official GLEIF API for newly issued Legal Entity Identifiers and returns one structured record per entity. It queries the live registry, filters and sorts by registration timeframe, country, and status, and paginates through results. Each record carries the LEI, registered legal name, country and jurisdiction, ISO legal-form code, entity and registration status, lifecycle dates, the issuing LOU and registration authority, plus a source citation and fetch timestamp. Note: it covers LEI-registered entities, not a complete formation feed.

Who it's for

KYC/AML teams screening new counterparties, entity-intelligence analysts tracking new registrations by country and legal form, and B2B researchers looking for fresh entity signals to drive outreach.

Sample fields / output

Field	Description
`lei`	20-char Legal Entity Identifier
`legalName`	Registered legal name
`country` / `jurisdiction`	Legal-address country + jurisdiction
`legalForm`	ELF legal-form code
`entityStatus` / `registrationStatus`	ACTIVE/INACTIVE · ISSUED/LAPSED
`initialRegistrationDate` / `lastUpdateDate`	LEI lifecycle dates
`managingLOU`	LEI-issuing organization
`sourceUrl` / `retrievedAt`	Citation + fetch timestamp

Example use cases

Screen and monitor newly registered counterparties for KYC/AML
Track new legal entities by country and legal form for entity intelligence
Detect fresh entity signals for B2B research and lead-gen

▶ Run GLEIF Legal Entity Monitor on Apify

Related actors

FAQ

Is this a complete company-formation feed?

No — it covers entities that register an LEI with GLEIF, not every new company everywhere.

Where does the data come from?

The official GLEIF API, queried live, with a source citation on every record.

What does it cost?

Pay-per-event: $0.005 per run plus $0.08 per entity.

New: Brand Mention Monitor — News & Web — track any brand or competitor across global news, no login

NexGenData — Fri, 05 Jun 2026 16:16:57 +0000

What it does

Brand Mention Monitor queries the official GDELT global news index to surface news and web mentions of any brand, keyword, or competitor. Each result comes back as a structured row — the matched query, headline, link, source domain, publication date, country, and language. It runs live against public news and web sources with no social platforms and no login to manage.

Who it's for

PR and comms teams watching coverage, competitive-intelligence and market researchers tracking rivals, and anyone wiring media mentions into reporting, alerts, or automated pipelines.

Sample fields / output

One row per mention, with these fields:


    query         // the brand/keyword matched
    title         // article headline
    url           // link to the article
    domain        // source domain
    seendate      // publication date
    sourcecountry // originating country
    language      // article language

Sample input: {"queries":["Stripe","Shopify"],"timespan":"7d","maxPerQuery":25}

Example use cases

Monitor PR and press coverage of your brand in near-real time
Track competitors and market topics for competitive intelligence
Feed media mentions into automated reporting or alerting pipelines

▶ Run Brand Mention Monitor on Apify

Related actors

FAQ

Does it need an API key or login?

No key, no login. It queries the public GDELT global news index.

How fresh are the results?

Live at run time — schedule it (n8n-ready) for recurring monitoring.

Does it cover social media?

No. It focuses on news and web sources, not social platforms.

New: SEC Filings to Markdown for RAG — EDGAR filings as clean, citation-tagged chunks for your LLM

NexGenData — Fri, 05 Jun 2026 16:16:49 +0000

What it does

SEC Filings to Markdown converts EDGAR filings (10-K, 10-Q, 8-K, 13F) into clean, chunked Markdown built for retrieval-augmented generation. It resolves a ticker to the issuer, pulls filings live from official EDGAR, strips scripts and styles, converts the HTML to ATX Markdown, and splits each document into configurable word-sized chunks. Every chunk carries citation metadata — accession number and source URL — alongside issuer identity, form type, and filing date.

Who it's for

Teams building financial-research copilots and RAG systems over filings, quants and fundamental analysts loading 10-Ks into vector stores, compliance teams building searchable filing knowledge bases, and fintech products that need LLM-ready text with citations.

Sample fields / output

Field	Description
`company` / `cik` / `ticker`	Issuer identity
`form`	Filing type (10-K, 10-Q, 8-K, 13F)
`filingDate`	Date filed
`accessionNumber`	SEC accession number (citation)
`sourceUrl`	Direct link to the source document
`chunkIndex` / `totalChunks`	Position within the filing
`markdown`	Clean Markdown chunk, ready for embedding

Example use cases

Build a financial-research copilot or RAG system over SEC filings
Load 10-Ks and other filings into a vector store for fundamental analysis
Stand up a searchable filing knowledge base for a compliance team

▶ Run SEC Filings to Markdown for RAG on Apify

Related actors

FAQ

Is the source data official?

Yes — filings are pulled live from official SEC EDGAR, no login required.

Why chunked Markdown instead of raw HTML?

Chunks are sized for embedding and each one is paired with citation metadata, so your LLM can cite the exact filing.

What does it cost?

Pay-per-event: $0.005 per run plus $0.04 per Markdown chunk.

New: Workday Jobs Scraper — Enterprise Careers, No Login — pull any company's Workday roles as clean rows

NexGenData — Fri, 05 Jun 2026 16:16:20 +0000

What it does

Workday Jobs Scraper extracts open roles from any company's Workday careers site (the *.myworkdayjobs.com address) by calling the public Workday CXS jobs endpoint. It paginates through every job page up to your cap and normalizes each role into one structured record — title, location, posting recency, employment type, and a direct apply URL. No login and no anti-bot to fight.

Who it's for

Recruiters sourcing enterprise roles at scale, GTM teams treating hiring as a buying signal, analysts tracking competitor headcount, and job boards building a clean enterprise feed.

Sample fields / output

Field	Description
`company` / `site`	Workday tenant + career site
`title`	Job title
`location`	Role location(s)
`postedOn`	Posting recency
`timeType`	Full-time / part-time
`url`	Direct apply URL


    { "company":"nvidia", "title":"Verification Engineer - GPU",
      "location":"India, Bengaluru", "postedOn":"Posted Today",
      "url":"https://nvidia.wd5.myworkdayjobs.com/.../job/..." }

Example use cases

Source enterprise roles at scale across many Workday tenants
Use hiring activity as a buying/intent signal for sales and GTM
Track a competitor's headcount growth or build a clean job-feed aggregator

▶ Run Workday Jobs Scraper on Apify

Related actors

FAQ

Do I need a Workday login or API key?

No. It uses each site's public CXS jobs endpoint — no login, no key.

How do I find a company's board URL?

Visit its careers page; the address will be a *.myworkdayjobs.com/... URL you paste straight in.

What does it cost?

Pay-per-event: $0.005 per run plus $0.08 per job, so about $40 for 500 roles.

New: B2B Lead List Cleaner — Dedupe, Normalize & MX-Verify — cut bounce rates before you import or send

NexGenData — Fri, 05 Jun 2026 16:15:43 +0000

What it does

B2B Lead List Cleaner takes a messy export and makes it outreach-ready: it normalizes every email, extracts the domain, validates syntax, and flags role-based and disposable addresses. It then runs an MX check over DNS-over-HTTPS, deduplicates by email, domain, or full row, and computes a deliverability indicator so you know which records are safe to keep. Original fields are preserved and each row is augmented with the cleaning metadata.

Who it's for

Anyone who buys, scrapes, or merges lead lists and pays for what happens next — SDRs and growth teams prepping a CRM import, RevOps cleaning merged sources, and anyone pre-flighting a list before paid enrichment or a send.

Sample fields / output


    { "email":"Jane.Doe@Stripe.com", "emailNormalized":"jane.doe@stripe.com",
      "domain":"stripe.com", "validSyntax":true, "roleAccount":false,
      "disposable":false, "mxValid":true, "isDuplicate":false,
      "deliverable":true, "issues":[] }

Every record keeps its original fields plus emailNormalized, domain, validSyntax, roleAccount, disposable, mxValid, isDuplicate, deliverable, and an issues array.

Example use cases

Clean an exported lead list before importing it into a CRM
Cut bounce rates by dropping invalid, role-based, or disposable emails
Dedupe lists merged from multiple sources, then pre-flight before paid enrichment

▶ Run B2B Lead List Cleaner on Apify

Related actors

FAQ

Does it send or store my emails?

No. It processes your list and returns cleaned records — it doesn't send anything or retain your data.

How is deliverability decided?

It combines syntax validity, non-disposability, and a live MX check into a single deliverable flag.

What does it cost?

Pay-per-event: $0.005 per run plus $0.05 per cleaned lead, so about $50 for 1,000 leads.

SEC Enforcement Actions API: Build a Compliance Watchlist

NexGenData — Fri, 05 Jun 2026 15:50:44 +0000

TL;DR. The SEC publishes every formal enforcement action as a Litigation Release (LR) on sec.gov, plus the underlying Administrative Proceedings (AP) and Civil Action complaints. These are public, free, and unstructured HTML. For compliance watchlists, AML name screening, law-firm conflict checks, or empirical research, you need them as structured data with defendants, charges, monetary sanctions, and case citations extracted. This guide covers the SEC's enforcement publication structure, the charge taxonomy that matters for downstream filtering, and a working Python pipeline using the SEC Litigation Releases actor.

How the SEC publishes enforcement actions

The SEC's Division of Enforcement issues three main public artifacts:

Litigation Releases (LR-XXXXX) — short narrative summaries of civil actions filed in federal court. New ones land at sec.gov/litigation/litreleases typically within 1–2 business days of filing.
Administrative Proceeding Releases (AP-XXXXX or Release No. XXXXX) — for actions resolved within the SEC's in-house administrative tribunal (cease-and-desist orders, suspensions, bars).
Complaint PDFs and Order PDFs — the underlying legal documents, linked from each release page.

The SEC's enforcement actions data page provides some aggregate statistics but does not expose a per-action JSON API. The release pages themselves are clean HTML — the parsing problem is moderate (defendant names, case citations, charge codes are not in consistent fields) but tractable.

The charge taxonomy that matters

For compliance watchlist use, you typically care about which statute and which conduct. The SEC's most-cited charging statutes:

Securities Act §17(a) — fraud in the offer or sale of securities
Exchange Act §10(b) / Rule 10b-5 — fraud in connection with the purchase or sale (insider trading, accounting fraud)
Investment Advisers Act §206 — fraud or deceit on advisory clients
Investment Company Act §17 — affiliated transactions, custody
FCPA — Exchange Act §13(b)(2) — books-and-records and internal-controls for foreign bribery cases
Whistleblower retaliation — Exchange Act §21F

For AML and law-firm conflict screening, the defendant identity matters more than the charge — you want clean entity and individual extraction with normalization (Smith vs. Smith Jr. vs. John A. Smith).

Working Python example

The SEC Litigation Releases actor normalizes defendants, extracts charging statutes, and parses monetary sanctions. Curl:


    curl -X POST "https://api.apify.com/v2/acts/nexgendata~sec-litigation-releases/run-sync-get-dataset-items?token=$APIFY_TOKEN" \
      -H "Content-Type: application/json" \
      -d '{"since": "2026-01-01", "includeDefendants": true, "maxResults": 500}'

Python — daily compliance watchlist match:


    import os, json, urllib.request

    APIFY_TOKEN = os.environ["APIFY_TOKEN"]
    ACTOR = "nexgendata~sec-litigation-releases"

    # Your firm's counterparty / customer list
    watchlist = {"acme capital management", "redwood securities llc", "john m doe"}

    payload = json.dumps({
        "since": "2026-05-01",
        "includeDefendants": True,
    }).encode("utf-8")

    url = f"https://api.apify.com/v2/acts/{ACTOR}/run-sync-get-dataset-items?token={APIFY_TOKEN}"
    req = urllib.request.Request(url, data=payload, method="POST",
                                  headers={"Content-Type": "application/json"})

    with urllib.request.urlopen(req, timeout=600) as r:
        releases = json.loads(r.read())

    for rel in releases:
        defendants = {d.lower() for d in rel.get("defendants", [])}
        hits = defendants & watchlist
        if hits:
            print(f"HIT: {rel['releaseNumber']} -- {hits}")
            print(f"   {rel['headline']}")
            print(f"   {rel['url']}")

Returned shape:


    {
      "releaseNumber": "LR-26031",
      "releaseDate": "2026-05-15",
      "headline": "SEC Charges Acme Capital and Its CEO with Securities Fraud",
      "defendants": ["Acme Capital Management LLC", "Jane Doe"],
      "charges": ["Securities Act 17(a)", "Exchange Act 10(b)", "Rule 10b-5"],
      "sanctions": {"disgorgement": 4500000, "civil_penalty": 2000000},
      "court": "S.D.N.Y.",
      "url": "https://www.sec.gov/litigation/litreleases/2026/lr26031.htm",
      "complaintUrl": "https://www.sec.gov/litigation/complaints/2026/comp26031.pdf"
    }

What you can build on top

AML name-screening append — augment your existing OFAC / sanctions screening with SEC enforcement defendants. Catches recently charged entities before they hit traditional adverse-media providers.
Law firm intake conflict check — automatic search of new clients against the past 24 months of SEC litigation.
Compliance trend monitoring — track which charging statutes are trending YoY (e.g., crypto-related Securities Act §17(a) cases since 2022, marketing rule enforcement since the 2024 amendments).
Empirical legal research — sanction-amount distributions by charge type, judge / jurisdiction patterns, settlement-vs-trial rates.

Comparison: SEC enforcement data sources

Source	Coverage	Structured fields	Cost
SEC.gov Litigation Releases (raw HTML)	Every LR + AP, back to 1995	Headline, date, URL — you parse the rest	Free
Westlaw / Lexis SEC No-Action	Includes underlying complaints and orders	Yes	$200–$600/seat/mo
NERA / Cornerstone empirical reports	Aggregate stats only	Yes — but aggregated	Public reports free; per-case data not redistributed
SEC Action Tracker (Stanford Securities Class Action Clearinghouse)	Securities class actions specifically	Yes	Free (academic)
SEC Litigation Releases actor	All LRs + linked APs	Defendants, charges, sanctions, court	PPE per release

Practical compliance pattern

Most RegTech compliance setups pair SEC enforcement data with FINRA BrokerCheck disclosures, OFAC sanctions, and adverse media. A nightly job pulling the past 30 days of LRs, deduping by release number, and intersecting against your watchlist is ~50 lines of Python and runs in under a minute.

Defendant-name normalization is the hard part

The SEC's litigation releases are written by enforcement staff and do not follow a single naming convention. "Acme Capital Management LLC" might also appear in another release as "Acme Capital Mgmt LLC" or "Acme Capital." Individual defendants get treated similarly — middle initials drop in and out, "Jr." appears in some releases and not others. For meaningful watchlist matching:

Normalize both the watchlist and the extracted defendant names: lowercase, strip punctuation, expand common abbreviations ("Mgmt" → "Management", "LLC" → "Llc" or stripped), drop trailing entity-type tokens for the comparison pass
Use fuzzy matching (Jaro-Winkler ≥ 0.92 is a sensible default) for the second pass
Always preserve the original strings for human review — never silently match
For individuals, include known aliases and prior names where you have them

The actor's defendants field is already post-normalized to a canonical form, which removes most of this work — but for high-stakes compliance (e.g., regulated bank AML programs) you'll still want a human review queue on every match.

Coverage of administrative proceedings

Litigation Releases cover SEC civil actions filed in federal court. Administrative Proceedings — actions resolved within the SEC's own administrative tribunal — are published separately as Releases No. (Securities Act, Exchange Act, Advisers Act, or Investment Company Act) and as orders. The bulk of SEC enforcement by case count actually settles through APs, not federal court, so for comprehensive compliance coverage you want both. The actor surfaces both streams.

For 8-K material-event surveillance alongside enforcement tracking, see our SEC 8-K filings guide. For monitoring private-placement issuers via Form D filings — useful for spotting suspect offerings before enforcement hits — see the Form D guide.

This same enforcement-watchlist pattern is what powers event-driven trading workflows on press release feeds — same architecture, different source.

Get started: Pull the past 90 days of SEC Litigation Releases at the actor page.

Use NexGenData With n8n: 15 Open-Source Community Nodes + 4 Ready-Made Workflow Templates

NexGenData — Thu, 04 Jun 2026 20:23:29 +0000

We just published an open-source n8n community node package, n8n-nodes-nexgendata, so you can build business-data, SEC-filing, press-release, and lead-enrichment workflows directly in n8n without writing HTTP-request boilerplate or hand-rolling polling for long-running jobs. Everything it touches is public-source data, and the package itself is free and MIT-licensed.

npm: n8n-nodes-nexgendata
GitHub (source + templates): TheNextGenNexus/n8n-nodes-nexgendata

Install in about two minutes

In n8n, go to Settings -> Community Nodes -> Install and enter n8n-nodes-nexgendata. Restart n8n and the nodes appear in the picker under a "NexGenData" search. You need one credential -- NexGenData API -- which is just your Apify Personal API token. That single credential unlocks all 15 nodes, and the token never leaves your n8n instance.

The 15 nodes

Email finding, validation and full deliverability verification; company enrichment; B2B lead search; website contact extraction; Google Maps, Yelp, Google Search, Redfin and LinkedIn-jobs scrapers; plus Lighthouse / Core Web Vitals analysis and Wappalyzer-style tech-stack detection. Browse the underlying tools in the NexGenData actor catalog.

4 ready-to-import workflow templates

The repo ships four importable workflow JSON files (use Import from File in n8n) so you go from install to a running flow quickly:

B2B lead pipeline -- find B2B leads, enrich the company, then verify the email, ready to drop into your CRM or spreadsheet. See also our lead generation & company enrichment guide.
SEC late-filing alert -- a daily check of SEC Form NT filings (companies that have told the SEC a report will be late), output to Slack, email, or Sheets. Background: how to monitor SEC late filings.
PR Newswire keyword monitor -- flag press releases matching your keywords (for example acquisition , partnership , product launch) on a schedule.
13F holdings-change alert -- track new and increased positions disclosed in institutional 13F filings (public SEC data), summarised daily. Background: how to track 13F holdings changes.

These pull from official public sources at run time, so freshness is set by your schedule. They are plumbing for monitoring and research workflows -- not investment advice, and nothing here is a trading signal or a guaranteed outcome.

Who it's for

Analysts and researchers, data teams, founders, lead-gen operators, compliance and research workflows, and automation builders -- anyone who wants public business, regulatory, market, or contact data flowing into n8n on a schedule without standing up and maintaining the scrapers themselves.

Explore the matching data clusters

Each node maps to a NexGenData tool family you can read more about: Lead Generation Data Tools, the SEC Filings Toolkit, and the Press Release Feed.

Cost

The package is free and MIT-licensed. The underlying tool runs are pay-per-use, billed by Apify directly to your own Apify account -- no markup, and new Apify accounts get monthly free credits that are enough to trial the nodes end to end.

FAQ

How do I install the NexGenData n8n nodes? In n8n, open Settings -> Community Nodes -> Install and enter n8n-nodes-nexgendata, then restart n8n. Add a NexGenData API credential using your Apify Personal API token.

What data do the nodes work with? Public-source data only -- company websites, public business directories, SEC EDGAR filings, and press-release feeds.

What does it cost? The package is free and MIT-licensed; the underlying tool runs are pay-per-use billed by Apify to your own account, with free monthly credits for new accounts.

Are the SEC and 13F templates investment advice? No. They are monitoring and research plumbing built on public filings, and nothing in them is a trading signal, recommendation, or guaranteed outcome.

Get the package and templates on GitHub ->

DTC Brand Competitor Analysis: Tracking Facebook Ad Creative at Scale

NexGenData — Thu, 04 Jun 2026 16:00:08 +0000

DTC Brand Competitor Analysis: Tracking Facebook Ad Creative at Scale

If you're running a direct-to-consumer brand, you know that competitive advantage often comes down to understanding what your rivals are actually doing in the market. But here's the problem: Facebook's ad library gives you visibility into what ads are running, but manually clicking through hundreds of ads from dozens of competitors is a time sink that drains resources from your actual marketing work.

What if you could automatically extract, categorize, and analyze your competitors' entire ad strategy in a fraction of the time? That's exactly what programmatic Facebook ad scraping enables. Let's explore how to turn competitor ad data into actionable insights.

Why Monitor Competitor Facebook Ads?

Before we dive into the technical side, let's establish why this matters. Facebook ad data reveals:

Creative Patterns: Which image styles, copy angles, and video formats are your competitors investing in? Are they running lifestyle photography or product closeups? Long-form storytelling or quick hooks?

Message Testing: You can see which CTAs competitors are emphasizing. Are they pushing "Shop Now," "Learn More," "Add to Cart," or "Join the Waitlist"? This signals what resonates with your shared audience.

Campaign Longevity: How long do successful ad campaigns stay live? If a competitor's ad has been running for three months, you're probably looking at a top performer. If ads rotate weekly, they might be testing harder or their conversion rates are lower.

Audience Segmentation Clues: Ad copy often hints at targeting. "For busy professionals" suggests they're reaching a specific demographic. "Finally, skincare that works for sensitive skin" reveals their audience pain point.

Budget Allocation: The volume and frequency of ads gives you a sense of marketing spend priority by product line or campaign.

For a skincare brand, this means you could see that competitors are heavily investing in anti-aging messaging in Q1, then shift to sun protection in spring. You could adapt your content calendar accordingly instead of guessing.

The Tracking Mode Advantage

Manual ad library browsing is tedious. You click, scroll, screenshot, record data in a spreadsheet. Scale that to monitoring 20 competitors across multiple markets, and suddenly you're looking at hours of manual work per week.

The Facebook Ads Scraper with tracker mode automates this entirely. Instead of manually visiting the ad library repeatedly, you set up monitoring parameters once, and the scraper continuously updates your data. New ads get added, paused ads get marked, and you receive structured JSON data you can immediately plug into your analytics systems.

Here's what automated tracking captures for each ad:

{
  "ad_id": "6284920485923948",
  "advertiser_name": "Luminous Beauty Co",
  "ad_creative": {
    "headline": "Clinically Proven Anti-Aging Serum",
    "body_text": "See visible results in 7 days or your money back. Trusted by 50,000+ customers.",
    "cta_button": "Shop Now",
    "primary_image_url": "https://example.com/ad-creative-xyz.jpg",
    "thumbnail_url": "https://example.com/thumbnail.jpg"
  },
  "ad_platforms": ["facebook", "instagram"],
  "first_seen": "2025-12-01T08:30:00Z",
  "last_updated": "2026-04-03T14:22:00Z",
  "status": "active",
  "estimated_reach": "250k - 500k",
  "demographics": {
    "age_range": "25-44",
    "gender": "female",
    "languages": ["english"],
    "interests": ["skincare", "wellness", "anti-aging"]
  }
}

With tracker mode, this data pipeline runs automatically. You're no longer guessing about competitor strategy—you're watching it unfold in real time.

Case Study: Analyzing Austin Skincare Brands

Let's walk through a practical example. Say you're entering the Austin skincare market with a sustainable, plant-based line. You identify five competitors who are actively advertising to Austin-area consumers:

Luminous Beauty (premium anti-aging focus)
Green Roots Botanicals (organic, eco-messaging)
Clear Skin Co (acne solutions for teens)
Glow Up Skincare (affordable all-in-one)
Wellness Dermatology (clinical/medical positioning)

You set up the Facebook Ads Scraper to monitor all five brands. Over two weeks, you collect data on 200+ active ads. Here's what the analysis reveals:

Creative Distribution (by count):

Anti-aging messaging: 42%
Acne/blemish solutions: 28%
General skincare/hydration: 20%
Sustainability/eco-messaging: 10%

CTA Patterns (what buttons they use):

"Shop Now": 55% of all ads
"Learn More": 25%
"Subscribe & Save": 15%
"Free Sample": 5%

Copy Angles (recurring themes):

Results-driven ("See results in 7 days"): 48%
Social proof ("50,000+ customers"): 35%
Problem-solution ("Finally, skincare that actually works"): 22%
Urgency ("Limited time offer"): 18%

Audience Targeting Insights:

All five brands target women 25-54
Three brands specifically target ages 35+
Two brands run separate teen-focused campaigns
All use interest-based targeting around "skincare," "wellness," or "beauty"

Campaign Longevity:

Luminous Beauty: Average ad lifespan 45 days (suggesting strong performers)
Green Roots: Average 18 days (testing more frequently)
Clear Skin: Average 60 days (entrenched campaigns)

From this data, you might conclude:

The market is saturated with results-driven messaging. To stand out, you could emphasize the unique angle that differentiates your brand—maybe it's the sustainability story that only Green Roots is pushing (and only at 10% of their ad volume).
"Shop Now" is table stakes, but the remaining 45% of your budget could experiment with other CTAs. "Subscribe & Save" is underutilized in your market—this could be an opportunity.
Anti-aging is the largest addressable segment, but you're entering a crowded space with established players. Your entry strategy might be better served by doubling down on a sub-segment (sustainable anti-aging) where there's less direct competition.
Campaign longevity matters. If competitors are running ads for 45+ days, they're probably seeing steady ROI. Your ads should be designed for durability, not just initial impact.

Building Your Own Monitoring System

Here's the workflow to set this up:

Step 1: Define Your Competitor Set

List 5-10 competitors (or adjacent brands). For a skincare company, this might include direct competitors plus adjacent categories (dermatology, medical spas, wellness supplements).

Step 2: Configure the Scraper

Using the Facebook Ads Scraper from Apify, set your parameters:

Search terms: Competitor brand names, relevant keywords ("sustainable skincare," "anti-aging serum")
Tracker mode: Enable continuous monitoring (daily or weekly updates)
Output format: JSON (easier to work with programmatically)

Step 3: Automate Processing

Write a simple script to ingest the JSON output:

import json
from datetime import datetime
from collections import Counter

def analyze_ads(json_data):
    ads = json.load(open(json_data))

    # Extract all CTAs
    ctas = [ad['ad_creative']['cta_button'] for ad in ads
            if 'cta_button' in ad['ad_creative']]
    cta_distribution = Counter(ctas)

    # Extract all themes from body text
    themes = extract_themes([ad['ad_creative']['body_text'] for ad in ads])

    # Calculate avg lifespan
    lifespans = []
    for ad in ads:
        if ad['status'] == 'paused':
            first_seen = datetime.fromisoformat(ad['first_seen'])
            last_updated = datetime.fromisoformat(ad['last_updated'])
            lifespan_days = (last_updated - first_seen).days
            lifespans.append(lifespan_days)

    avg_lifespan = sum(lifespans) / len(lifespans) if lifespans else 0

    return {
        'total_ads_monitored': len(ads),
        'cta_distribution': dict(cta_distribution),
        'average_campaign_lifespan_days': round(avg_lifespan),
        'active_campaigns': len([a for a in ads if a['status'] == 'active'])
    }

def extract_themes(texts):
    keywords = ['results', 'proven', 'customers', 'quality', 'sustainable',
                'natural', 'effective', 'formula']
    theme_matches = Counter()

    for text in texts:
        text_lower = text.lower()
        for keyword in keywords:
            if keyword in text_lower:
                theme_matches[keyword] += 1

    return theme_matches

# Run analysis
insights = analyze_ads('competitor_ads_export.json')
print(json.dumps(insights, indent=2))

This script transforms raw ad data into structured insights you can use in strategic planning.

Step 4: Set Up Recurring Monitoring

Configure the scraper to run weekly. This means you're always working with 7-14 days of fresh data, not stale snapshots.

Step 5: Build Your Insights Dashboard

Feed the processed data into a BI tool, Notion database, or spreadsheet. Track trends over time:

Which creative approaches are gaining traction?
Are your competitors' ad budgets increasing or decreasing by category?
Which messaging angles are being A/B tested across multiple brands?

What This Data Actually Tells You

Here's the honest take: Ad data is a signal, not a prescription. Just because your competitor is running a specific creative approach doesn't mean it works. It might be a failed experiment they haven't paused yet. It might be a top performer, or it might be running because it's already paid for itself and they're just letting it coast.

What you're really doing is pattern matching at scale. You're identifying what the market is testing and validating against. If five competitors are all emphasizing sustainability, that's a signal that the audience cares about it. If one competitor is, that might be a differentiator opportunity.

The competitive advantage comes from seeing patterns faster than your competitors can manually identify them, then moving to counter-strategies or market gaps before others notice. If you can execute on insights within a week, and your competitors are still manually reviewing ads, you've gained a meaningful edge.

Getting Started

The Facebook Ads Scraper supports tracker mode, which is the key to scaling this work. Set it up once, let it run continuously, and focus your analysis energy on interpreting the data rather than collecting it.

Start with your top three competitors. Run a two-week tracking period. Analyze the patterns. Make one strategic decision based on the insights. Then iterate.

That's how competitive analysis becomes a competitive advantage.

ZeroBounce Alternative: Verify Email Deliverability Pay-Per-Result, No Subscription

NexGenData — Wed, 03 Jun 2026 18:15:59 +0000

If you've shopped for email verification, you've hit the same wall I have: ZeroBounce, NeverBounce, Kickbox, and BriteVerify all gate the good stuff behind a monthly subscription or a minimum spend. ZeroBounce starts around $16/mo, NeverBounce and Kickbox around $8/mo, and the per-email rates only get good once you commit to volume. That's fine if you verify lists every week. If you verify occasionally — before a campaign, after a scrape, at signup — you're paying a subscription to use something twice a month.

Here's the honest version of how to get the same core verification on a pure pay-per-use basis, and exactly when it's the right call (and when it isn't).

What email verification actually checks

Every verifier — paid or not — runs the same handful of checks. There's no secret sauce; the accuracy comes from doing these correctly:

Syntax — is the address even well-formed?
MX records — does the domain actually accept mail?
SMTP RCPT probe — does the mailbox exist? (A read-only RCPT TO handshake; it never delivers a message.)
Catch-all detection — does the domain accept everything, making per-mailbox checks unreliable?
Disposable/temp-mail flag — is it a throwaway domain?
Role-account flag — info@, sales@, support@ (low engagement, high complaint risk)

A good verifier returns a clear valid / invalid / risky / unknown status plus the reason, so you can decide what to suppress versus send-with-caution.

The pay-per-use option

Instead of a subscription, you can run verification as an Apify actor and pay per result — no plan, no monthly minimum, no per-seat fee, and it runs inside an account you may already have (REST API, Python/JS SDKs, Zapier/Make/n8n, or as an MCP tool for AI agents).

Email Verification Tool — Deliverability Checker does exactly the checks above and returns the full breakdown per address:

from apify_client import ApifyClient
client = ApifyClient("YOUR_APIFY_TOKEN")
run = client.actor("nexgendata/email-verification-tool").call(run_input={
    "emails": ["jane@stripe.com", "fake@nonexistentdomain12345.com"]
})
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item["email"], item["status"], item["reason"])

For bulk list hygiene (CSV in → clean CSV out) there's the companion Bulk Email Validator. And if you're verifying emails you don't have yet, Company Email Finder discovers and pattern-validates business addresses from a domain. Chain finder → verifier and you've rebuilt the core of a paid stack with no subscription attached.

The honest part: when pay-per-use wins, and when it doesn't

I'm not going to tell you it's cheaper per email — it isn't always. Per-email rates on the subscription tools get very low at volume. The pay-per-use math wins on a different axis: commitment.

Pay-per-use wins when you verify intermittently, in bursts, or programmatically: a 500-address list once a month, signup-form checks that spike and idle, a one-off scrape cleanup. You pay for those results and nothing the rest of the month — no $8–96/mo floor for software you barely touched.
A subscription wins when you verify large lists every week. At that cadence the per-email discount and included quota beat metered pricing. If that's you, buy the plan.

So the real question isn't "which is cheapest per email" — it's "do I verify often enough to justify a standing subscription?" For a lot of small teams, agencies juggling client lists, and anyone wiring verification into an automation, the answer is no.

Don't skip the compliance + deliverability basics

Verification protects your sender reputation, but it doesn't grant permission to send. A few things the vendors' landing pages gloss over:

Verify before every send, not once. Addresses decay ~2–3%/month. A list verified in January is dirty by spring.
Treat catch-all and role as send-with-caution, not green lights.
You still need a lawful basis for outreach (CAN-SPAM in the US, GDPR/PECR in the EU/UK) — clear identification and an easy opt-out. Clean ≠ consented.
Warm up new sending domains. A pristine list blasted from a cold domain still lands in spam.

Bottom line

If you verify email constantly, a subscription tool is genuinely the better buy. If you verify occasionally or programmatically, paying per result with no plan and no minimum is the cheaper, simpler path — same SMTP/MX/catch-all/disposable checks, billed only when you actually run it.

Email verification touches deliverability and privacy obligations. Confirm your lawful basis and honor opt-outs — this is list hygiene, not a license to send.

Cheap ZoomInfo Alternative: B2B Contact Data From Public Sources (No Annual Seat)

NexGenData — Wed, 03 Jun 2026 17:55:38 +0000

ZoomInfo, Apollo, and Lusha are how most teams buy B2B contact data — and they're priced like it: annual seats, steep minimums, and "talk to sales" pricing that doesn't fit a small team or a one-off campaign. The good news: for a lot of B2B prospecting you can assemble the same core data — company domains, contact emails, firmographics — from public sources at pay-per-use cost. Here's the honest version of how, and where it does and doesn't work.

What "contact data" actually breaks into

A usable B2B lead record is really three separate problems:

Find the company (domain, industry, size, location)
Find the people / emails at that company
Verify the emails so you're not blasting a dead list

You don't need an enterprise seat for any of these — you need the right public source for each.

1. Company → domain + firmographics

Most enrichment starts from a company name or domain. Public sources cover a surprising amount: a company's own site (industry, about, careers → headcount signal), the global LEI registry for legal identity, and SEC/registry data for anything filed. Pull those and you have the firmographic shell most outreach actually segments on (industry, size band, location).

2. Company → emails

This is where the paid tools earn their keep, but it's also the most reproducible part. Business email follows patterns — first.last@, flast@, first@ — and you can discover and validate the live ones for a domain rather than buying a static list. Pattern discovery + per-address validation gets you most of the way for direct, role-based, and named contacts.

The honest caveats (the ones the glossy vendors skip):

Coverage is not 100%. Public-source discovery is excellent for SMBs and mid-market with normal mail setups; it's weaker where a company hides behind a catch-all or a privacy proxy.
Always verify. An unverified email is a bounce waiting to happen and a sender-reputation hit. Validate before you send.
Stay compliant. B2B outreach is legal in most places, but CAN-SPAM (US) and GDPR/PECR (EU/UK) have real rules — lawful basis, clear identification, easy opt-out. Build the list responsibly; don't scrape personal data you've no basis to process.

3. Verify

Run every address through validation (syntax, MX, deliverability signals) and drop the risky ones. A smaller verified list beats a big bouncy one every time — your deliverability and your domain reputation depend on it.

The turnkey version

Stitching those three is the work. If you'd rather not, these pay-per-use actors do each piece — no annual seat, no minimums:

Company Email Finder — domain → discovered, pattern-validated business emails
B2B Leads Finder — find contacts by job title, company, and industry
Company Enrichment Tool — company → domain, firmographics, socials, employee band

Chain them — enrich the company, find the contacts, validate the emails — and you've rebuilt the core of a ZoomInfo workflow at pay-as-you-go cost, with full control over sourcing and compliance.

B2B contact data carries real legal and privacy obligations (CAN-SPAM, GDPR, PECR). Confirm your lawful basis and honor opt-outs — this is prospecting infrastructure, not a license to spam.

Free Crunchbase Alternative: Company + Funding Data from Official Sources (No API Key)

NexGenData — Wed, 03 Jun 2026 16:56:11 +0000

If you've priced Crunchbase or PitchBook lately, you know company and funding data is mostly locked behind expensive seats. But a surprising amount of it is public and free — you just have to pull it from the primary sources instead of a reseller. This guide shows how to assemble a company profile (legal identity + real funding rounds + financials) from the SEC and the global LEI system, with no API key and no scraping of gated sites.

1. Funding rounds → SEC Form D

When a private US company raises a round under Regulation D (most venture and private placements), it files a Form D with the SEC. It's a public filing, and it contains the parts you actually want: total offering amount, amount sold, industry, and date.

Find a company's Form D filings via EDGAR full-text search, then pull the offering amounts from the filing's XML:

# 1) find Form D filings for an issuer
curl -s 'https://efts.sec.gov/LATEST/search-index?q=%22Databricks%22&forms=D' \
  -H 'User-Agent: yourname you@example.com'

# 2) each hit has a CIK + accession; fetch the Form D primary_doc.xml
curl -s 'https://www.sec.gov/Archives/edgar/data/<CIK>/<ACCESSION_NODASHES>/primary_doc.xml' \
  -H 'User-Agent: yourname you@example.com'

The XML carries <totalOfferingAmount>, <totalAmountSold>, and <industryGroupType>. A recent Databricks Form D, for example, discloses an offering north of $1B — real funding data, filed by the company, free to read.

Two honest caveats (these matter, and most scrapers get them wrong):

Match the issuer name strictly. Full-text search returns anything mentioning the term. Stripe Milton LLC is not Stripe; a fund named after a startup is not the startup. Normalize and require an exact legal-name match, and exclude SPV/fund vehicles.
Coverage is partial. Plenty of famous startups raise through structures that never file a Form D under their own name. Form D is excellent where it exists — don't treat its absence as "no funding."

Always send a declared User-Agent with contact info; that's SEC's fair-access requirement.

2. Legal identity → GLEIF (the LEI system)

The Global Legal Entity Identifier Foundation publishes legal-entity data as fully open data — legal name, HQ country, registered address, status. Resolve a brand name to an entity:

# fuzzy-match a name to candidate LEIs
curl -s 'https://api.gleif.org/api/v1/fuzzycompletions?field=entity.legalName&q=Anthropic'

# then fetch the full record
curl -s 'https://api.gleif.org/api/v1/lei-records/<LEI>'

Caveat: common brand names collide (there can be several "Stripe" entities in different countries), so confirm the country/jurisdiction before trusting a match — ideally cross-check against a US SEC filing.

3. Industry + financials → SEC EDGAR

For any SEC-registered company (public companies and Form D filers), the submissions API gives you SIC industry, state of incorporation, business address, former names, and tickers:

curl -s 'https://data.sec.gov/submissions/CIK0000320193.json' -H 'User-Agent: yourname you@example.com'

For public companies, XBRL company-facts give real reported numbers (Apple's latest annual revenue comes back as ~$416B):

curl -s 'https://data.sec.gov/api/xbrl/companyconcept/CIK0000320193/us-gaap/Revenues.json' \
  -H 'User-Agent: yourname you@example.com'

Putting it together

Stitch those three and you have a legitimate, official-source company profile — identity, funding signals, and financials — without paying for Crunchbase and without scraping anything gated. The hard part is the glue: entity resolution, strict issuer matching, XML parsing, and rate-limit-friendly EDGAR access.

If you'd rather skip the plumbing, the Company Data Aggregator actor does exactly this in one call — give it a company name or domain and it returns GLEIF legal identity, SEC Form D funding signals, EDGAR industry/financials, and a domain/tech profile. No API key.

Useful neighbors if you're building company intelligence:

SEC Form 8-K Material Events Scraper — every material corporate event
13F Holdings Delta Tracker — institutional position changes
Business Registration Lookup — global registry data

All of this is informational, official-source data — not investment advice. Build responsibly, declare your User-Agent, and respect the SEC's rate limits.

How to Vet a Stockbroker with FINRA BrokerCheck: Free API Guide

NexGenData — Wed, 03 Jun 2026 15:31:01 +0000

TL;DR. FINRA BrokerCheck is the public registration database for every individual securities broker (Series 7, Series 63, etc.) and FINRA-member firm in the United States. You can search by name, CRD number, or firm. It returns employment history, exam record, registration status, and — critically — disclosures (customer complaints, regulatory actions, terminations for cause, bankruptcies, criminal matters). FINRA does not publish a documented public API, but the BrokerCheck site is backed by a JSON endpoint you can query. This guide covers what BrokerCheck contains, what disclosures actually mean, how to query the underlying endpoint, and a working Python implementation that handles pagination and disclosure parsing.

Who needs to query BrokerCheck programmatically

RIA and broker-dealer recruiters screening candidates against their U4 history before extending an offer.
Compliance teams at brokerage firms monitoring their own reps for new disclosures (a U4 amendment hits BrokerCheck within ~15 days).
Wealth platforms and fintechs verifying advisor identities at onboarding.
Investor due diligence — family offices, RIAs vetting subadvisors, journalists investigating misconduct patterns.
Academic researchers studying broker misconduct (the Egan-Matvos-Seru "Market for Financial Adviser Misconduct" papers run on BrokerCheck-derived data).

What BrokerCheck actually contains

Per FINRA's own documentation, BrokerCheck pulls from the Central Registration Depository (CRD) and the Investment Adviser Registration Depository (IARD). For each individual you get:

CRD number (the canonical identifier)
Current registration status (Registered, Not Registered, Pending)
Currently registered firm(s) and self-regulatory organizations (FINRA, MSRB, individual state regulators)
Employment history (last 10 years)
Exam record (Series 7, 63, 65, 24, etc., with pass dates)
Disclosure events — the section that matters most for vetting

For firms you additionally get the firm's BD or IA registration status, branch office count, and aggregated disclosure history.

The disclosure taxonomy

"Disclosure" in BrokerCheck-speak means a reportable event on the broker's U4 form. FINRA's U4 instructions classify them as:

Customer Dispute — settled, pending, or denied complaint involving sales practice
Regulatory Action — FINRA, SEC, state, or foreign regulator sanction
Employment Separation After Allegations — fired or resigned during an internal review
Criminal — felony or specified misdemeanor
Civil — non-customer civil litigation
Financial — bankruptcy, compromise with creditors, unsatisfied judgments
Judgment/Lien — outstanding judgment or lien
Investigation — current FINRA or SEC investigation

Customer disputes and regulatory actions are the categories that correlate with future misconduct in the academic literature. A broker with two or more disclosures has materially higher odds of future complaints.

The undocumented JSON endpoint

BrokerCheck's frontend is a React SPA backed by JSON endpoints under https://api.brokercheck.finra.org/. The endpoints are not officially documented but are public, unauthenticated, and stable. The search call:


    GET https://api.brokercheck.finra.org/search/individual?query=Smith&hl=true&nrows=12&start=0&r=25&sort=score+desc

Returns a Solr-style JSON response with hits containing CRD numbers, names, firms, and disclosure counts. To pull a specific individual's full profile:


    GET https://api.brokercheck.finra.org/search/individual/{CRD}?hl=true

FINRA's terms of service prohibit "scraping" in the colloquial commercial-redistribution sense. Querying these public endpoints for compliance, due diligence, or research is consistent with how the site is designed to be used. Polite rate limiting (1–2 requests/second), a real User-Agent, and not redistributing raw FINRA data wholesale are the practical guardrails.

Working Python example

The FINRA BrokerCheck Search actor wraps the endpoint with pagination, disclosure normalization, and rate limiting. Curl:


    curl -X POST "https://api.apify.com/v2/acts/nexgendata~finra-brokercheck-search/run-sync-get-dataset-items?token=$APIFY_TOKEN" \
      -H "Content-Type: application/json" \
      -d '{"queries": ["John Smith"], "type": "individual", "includeDisclosures": true}'

Python — screening a candidate list before recruiting:


    import os, json, urllib.request

    APIFY_TOKEN = os.environ["APIFY_TOKEN"]
    ACTOR = "nexgendata~finra-brokercheck-search"

    candidates = ["Jane Doe", "John Smith", "Maria Garcia"]

    payload = json.dumps({
        "queries": candidates,
        "type": "individual",
        "includeDisclosures": True,
        "includeEmploymentHistory": True,
    }).encode("utf-8")

    url = f"https://api.apify.com/v2/acts/{ACTOR}/run-sync-get-dataset-items?token={APIFY_TOKEN}"
    req = urllib.request.Request(url, data=payload, method="POST",
                                  headers={"Content-Type": "application/json"})

    with urllib.request.urlopen(req, timeout=300) as r:
        profiles = json.loads(r.read())

    for p in profiles:
        disc_count = len(p.get("disclosures", []))
        flag = "RED FLAG" if disc_count >= 2 else "OK" if disc_count == 0 else "REVIEW"
        print(f"{flag} | CRD {p['crd']} | {p['name']} | disclosures={disc_count}")

How this compares to commercial vetting tools

Tool	Source	Coverage	Per-check cost
HireRight Securities Industry	FINRA + criminal + credit	US securities	~$50–$120 per check (volume-priced)
Sterling Securities + Financial	FINRA + state regs	US + some intl	~$40–$100 per check
FINRA BrokerCheck (web UI)	FINRA CRD	US registered reps and firms	Free, manual
BrokerCheck actor (Apify)	FINRA CRD via JSON endpoint	Same as BrokerCheck	PPE — cents per query at volume

BrokerCheck does not replace a full background check — it has no criminal-records search, no credit, no education verification. For full pre-hire screening you still need HireRight, Sterling, or an equivalent. BrokerCheck programmatic access is the right tool for the specific job of "does this person have a clean FINRA record, and has anything new shown up since the last time I checked."

Ongoing monitoring pattern

A common compliance workflow: nightly job that pulls the current disclosure count for each of your firm's reps, compares to last night's snapshot, and alerts on any delta. A 100-rep firm runs that for under $5/month at PPE rates. The same pattern works for monitoring a watch list of competitor brokers if you're recruiting laterally.

What the data is good for — and what it isn't

BrokerCheck is canonical for what it covers: FINRA-registered brokers and broker-dealer firms. It is not canonical for:

Investment advisers (RIAs) — those are in IAPD. There is overlap (dually registered "hybrid" reps appear in both) but the U4 disclosure scope differs from the Form ADV Part 2B brochure supplement disclosure scope. Don't conflate them.
Insurance-only producers — state insurance department licenses, not FINRA.
Crypto / digital-asset firms not registered as broker-dealers — outside FINRA's perimeter unless they're FINRA members.
Former brokers — partial. BrokerCheck retains records for 10 years after a person's last registration ended, but disclosure history that was old at the time of departure may be incomplete.

For a complete picture on a senior advisor who has moved between BD and RIA registrations, you'll usually pull both BrokerCheck and IAPD and merge by CRD number (the CRD is shared across both systems).

Refresh cadence

U4 amendments must be filed by the member firm within 30 days of the broker learning of a reportable event (10 days for some categories). FINRA publishes the amendments on BrokerCheck within roughly 15 days after filing. Practically this means a weekly polling cadence catches every disclosure within ~30–45 days of the underlying event. For high-stakes monitoring (e.g., a wealth platform that custodies client assets through specific reps), nightly polling reduces the window to ~16 days end-to-end.

Related ground

BrokerCheck's institutional analogue is IAPD (Investment Adviser Public Disclosure) for SEC-registered investment advisors. For enforcement actions against brokers and firms, see our companion guide on SEC enforcement actions tracking. For Singapore-based equivalents, the Monetary Authority of Singapore publishes enforcement actions tracked by our MAS enforcement actor.

Get started: Run your first BrokerCheck batch free at the FINRA BrokerCheck Search actor. Bring a list of names or CRDs.

DEV Community: NexGenData

New: GLEIF Legal Entity Monitor — Newly Registered LEIs Worldwide — catch freshly registered legal entities worldwide

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New: Brand Mention Monitor — News & Web — track any brand or competitor across global news, no login

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New: SEC Filings to Markdown for RAG — EDGAR filings as clean, citation-tagged chunks for your LLM

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New: Workday Jobs Scraper — Enterprise Careers, No Login — pull any company's Workday roles as clean rows

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Do I need a Workday login or API key?

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New: B2B Lead List Cleaner — Dedupe, Normalize & MX-Verify — cut bounce rates before you import or send

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Does it send or store my emails?

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SEC Enforcement Actions API: Build a Compliance Watchlist

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Defendant-name normalization is the hard part

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Install in about two minutes

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4 ready-to-import workflow templates

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ZeroBounce Alternative: Verify Email Deliverability Pay-Per-Result, No Subscription

What email verification actually checks

The pay-per-use option