Talent Intelligence Report

Talent intelligence report generator that produces a composite workforce dossier for any company from 7 parallel data sources: job postings, USPTO patents, EPO patents, ORCID researcher profiles, company research, GitHub repositories, and SEC insider trading filings. Built for investors, M&A teams, and competitive strategy analysts who need a defensible, data-driven view of a company's human capital health before making decisions.

The actor runs all 7 data collection tasks simultaneously, then applies four independent scoring models — Talent Velocity, Brain Drain Index, Competitive Capability Map, and Executive Flight Risk — to produce a weighted composite score (0-100) and an INVEST/MONITOR/CAUTION/AVOID verdict. One run replaces days of manual research across seven separate tools and databases.

What data can you extract?

Why use Talent Intelligence Report?

Building a workforce intelligence picture manually means visiting LinkedIn, EDGAR, USPTO, ORCID, GitHub, and multiple job boards separately, then synthesizing what you find by hand. A thorough analyst can spend 2-3 days on a single company. When evaluating a portfolio of 10 targets before a fund close, that workload becomes impossible at deal pace.

This actor automates the entire process. Enter a company name, click Start, and receive a structured dossier in minutes — with scored dimensions, detected signals, and an investment-grade verdict.

• Scheduling — run monthly workforce scans on every portfolio company to track talent health over time
• API access — trigger from Python, JavaScript, or any HTTP client to embed in investment workflows
• Proxy rotation — scrape at scale without IP blocks using Apify's built-in proxy infrastructure
• Monitoring — get Slack or email alerts when a portfolio company's score drops below a threshold
• Integrations — pipe results into Zapier, Make, Google Sheets, HubSpot, or webhooks

Features

• 7 parallel data sources — job market intelligence, USPTO patents, EPO patents, ORCID researcher profiles, company deep research, GitHub repositories, and SEC Form 4 insider trading all queried simultaneously to minimize run time
• Talent Velocity scoring (0-100) — job volume and seniority mix (max 40), Herfindahl-Hirschman Index (HHI) hiring concentration analysis (max 25), GitHub technical momentum from recent repository activity (max 20), and company growth signals from funding and expansion mentions (max 15)
• Brain Drain Index (0-100, inverted) — patent output decline by comparing recent vs. older filings, researcher mobility via ORCID multi-affiliation detection, SEC sell/buy ratio as executive attrition proxy, and a low-hiring amplifier that escalates risk when headcount contraction and insider selling occur together
• Competitive Capability Map (0-100) — technology domain extraction across 10 categories (AI/ML, Cloud, Blockchain, Cybersecurity, Mobile, Data/Analytics, Frontend, Backend, DevOps) from job titles, patent titles, and GitHub repository topics
• Executive Flight Risk model (0-100, inverted) — Form 4 sell/buy ratio with large-transaction detection (>$1M flagged individually), serial seller identification (3+ transactions per executive named explicitly), executive replacement hiring signals (CEO, CTO, CFO, COO open roles), and company distress indicators including layoffs, losses, and regulatory investigations
• Composite scoring with override logic — weighted formula: Talent Velocity 25%, Brain Drain inverted 25%, Competitive Capability 30%, Executive Flight inverted 20%; automatic AVOID override when flight risk is CRITICAL or brain drain is HEMORRHAGING regardless of other dimension scores
• 5-tier growth classification — CONTRACTING, STABLE, GROWING, SURGING, HYPERGROWTH for Talent Velocity; RETAINING, STABLE, AT_RISK, DRAINING, HEMORRHAGING for Brain Drain; NASCENT, DEVELOPING, COMPETITIVE, LEADING, DOMINANT for Capability; LOW, MODERATE, ELEVATED, HIGH, CRITICAL for Flight Risk
• Named signal strings — every scoring decision produces a human-readable signal (e.g., "18 insider sells vs 5 buys — executives liquidating positions") that can be used directly in investment memos
• Sample raw data included — top 10 job postings, top 10 patents, top 10 GitHub repos, and up to 15 insider transactions included in output for verification and further analysis
• Partial private company support — job postings, patents, researchers, and GitHub data are available for private companies; SEC data is only available for public entities

Use cases for talent intelligence report

Investment due diligence

Venture capital analysts and growth equity investors can run a talent intelligence report on any target before a term sheet. The four-dimension score structure maps directly to standard investment frameworks: talent velocity predicts execution capacity, brain drain predicts retention risk, competitive capability maps IP moats, and executive flight risk surfaces instability before it appears in financial statements. A CAUTION or AVOID verdict early in the pipeline saves weeks of deeper diligence on poor prospects.

M&A human capital due diligence

Acquirers frequently discover hidden talent risks after close. Running a talent intelligence report on an acquisition target during the LOI phase surfaces brain drain indicators, key inventor attrition, and executive divestment patterns that rarely appear in management presentations. The patent decline analysis comparing recent vs. older filings is particularly effective at detecting IP creation slowdowns caused by researcher departures.

Competitive intelligence and market mapping

Strategy teams at technology companies can run talent intelligence reports on 5-10 competitors simultaneously to map the competitive landscape. The tech domain extraction from job titles and GitHub repository topics reveals where competitors are building capability before products ship. Hiring function concentration analysis (HHI) shows whether a competitor is making a focused bet on one function or scaling broadly.

Portfolio monitoring for institutional investors

Public equity funds and hedge funds holding positions in 20-50 companies can schedule monthly talent intelligence runs to track workforce health as a leading indicator. The executive flight risk dimension — tracking Form 4 sell/buy ratios and large transaction patterns — often leads published financial deterioration by one to two quarters. Automated score alerts enable rapid position review before the broader market reacts.

Executive search and leadership advisory

Retained search firms and executive advisors can identify leadership transition opportunities by monitoring companies for elevated executive flight risk signals. A score flagging multiple C-suite openings alongside high insider sell ratios indicates an organization rebuilding its leadership layer — a high-value sourcing scenario for senior placements.

Startup benchmarking and talent market research

Founders and HR leaders can benchmark their company's talent velocity and competitive capability score against known peers. Understanding whether your hiring concentration HHI is above or below sector norms, and whether your GitHub technical momentum is keeping pace with competitors, provides a quantitative foundation for workforce planning conversations with boards.

How to generate a talent intelligence report

1. Enter the company name — type the company you want to analyze into the companyName field. Use the legal or trading name (e.g., "Stripe", "Palantir Technologies", "Databricks") for best results across all 7 data sources.
2. Add optional context — enter an industry (e.g., "fintech", "healthcare") and region (e.g., "United States", "San Francisco Bay Area") to sharpen job market search results. These are optional but improve matching for common company names.
3. Click Start and wait — the actor queries all 7 data sources in parallel. Most runs complete in 2-5 minutes. Complex companies with large patent portfolios or many job postings may take up to 10 minutes.
4. Download your dossier — open the Dataset tab, then export as JSON, CSV, or Excel. The composite score, verdict, and all signal strings are in the top-level fields for immediate use in memos or dashboards.

Input parameters

Input examples

Standard company analysis:

{
  "companyName": "Stripe",
  "industry": "fintech",
  "region": "United States"
}

Competitor landscape scan (run once per company):

{
  "companyName": "Palantir Technologies",
  "industry": "data analytics",
  "region": "United States"
}

Minimal run — name only:

{
  "companyName": "Databricks"
}

Input tips

• Use the full trading name — "Meta Platforms" returns more complete patent and SEC data than just "Meta". For startups, try both the legal entity name and the product name.
• Add region for common names — "Acme Corp" without a region will return job data from multiple unrelated companies. Adding "United States, Austin TX" narrows the job market search.
• Batch comparisons in sequence — run one company per actor call and compare composite scores across runs. The standardized 0-100 scale makes direct comparison meaningful.
• Industry context improves job matching — the industry field is appended to the job market query, so "Stripe fintech" returns more relevant postings than "Stripe" alone in ambiguous markets.

Output example

{
  "reportType": "Talent Intelligence Report",
  "generatedAt": "2026-03-20T09:41:00.000Z",
  "entity": "Stripe",
  "industry": "fintech",
  "region": "United States",
  "compositeScore": 74,
  "verdict": "INVEST",
  "talentVelocity": {
    "score": 78,
    "totalOpenings": 52,
    "seniorRoles": 14,
    "techRoles": 33,
    "growthSignal": "SURGING",
    "signals": [
      "52 open positions — aggressive hiring campaign",
      "14 senior/leadership roles — building new org layers",
      "33 technical roles — major engineering investment",
      "Hiring across 6 functions — broad organizational growth"
    ]
  },
  "brainDrain": {
    "score": 22,
    "inventorCount": 48,
    "researcherCount": 11,
    "patentActivity": 31,
    "drainLevel": "STABLE",
    "signals": [
      "11 active GitHub repos — strong open-source/tech presence"
    ]
  },
  "competitiveCapability": {
    "score": 81,
    "techDomains": ["Backend", "Data/Analytics", "Cloud", "AI/ML", "Cybersecurity", "DevOps"],
    "patentStrength": 31,
    "talentDepth": 18,
    "capabilityLevel": "DOMINANT",
    "signals": [
      "Active across 6 tech domains: Backend, Data/Analytics, Cloud, AI/ML, Cybersecurity, DevOps",
      "31 patents — strong IP portfolio",
      "Patents in both US and EU — global IP strategy"
    ]
  },
  "executiveFlight": {
    "score": 18,
    "insiderSells": 4,
    "insiderBuys": 9,
    "sellRatio": 0.31,
    "riskLevel": "LOW",
    "signals": []
  },
  "keyRisks": [],
  "keyStrengths": [
    "Strong talent velocity — aggressive hiring signals",
    "Talent retention healthy — low brain drain indicators",
    "Strong competitive position — deep IP and tech capabilities",
    "Executive stability — insiders holding/buying"
  ],
  "allSignals": [
    "52 open positions — aggressive hiring campaign",
    "14 senior/leadership roles — building new org layers",
    "33 technical roles — major engineering investment",
    "Active across 6 tech domains: Backend, Data/Analytics, Cloud, AI/ML, Cybersecurity, DevOps",
    "31 patents — strong IP portfolio",
    "Patents in both US and EU — global IP strategy"
  ],
  "dataSources": {
    "jobPostings": 52,
    "usPatents": 24,
    "euPatents": 7,
    "researchers": 11,
    "companyIntel": 3,
    "githubRepos": 18,
    "insiderTransactions": 13
  },
  "sampleData": {
    "topJobPostings": [...],
    "recentPatents": [...],
    "topGithubRepos": [...],
    "insiderTransactions": [...]
  }
}

Output fields

How much does it cost to run a talent intelligence report?

Talent Intelligence Report uses pay-per-run pricing — each report costs approximately $0.25-$0.60 depending on data volume. Platform compute costs are included. Apify's free tier provides $5 of monthly credits, enough for 8-20 reports.

You can set a maximum spending limit per run to control costs. The actor stops when your budget is reached.

Compare this to LinkedIn Talent Insights at $599+/month or PitchBook at $24,000+/year — with Talent Intelligence Report, a quarterly portfolio scan of 50 companies costs approximately $20 with no subscription commitment.

Talent intelligence report using the API

Python

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")

run = client.actor("ryanclinton/talent-intelligence-report").call(run_input={
    "companyName": "Stripe",
    "industry": "fintech",
    "region": "United States"
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"Company: {item['entity']}")
    print(f"Composite Score: {item['compositeScore']}/100 — Verdict: {item['verdict']}")
    print(f"Talent Velocity: {item['talentVelocity']['score']} ({item['talentVelocity']['growthSignal']})")
    print(f"Brain Drain: {item['brainDrain']['drainLevel']}")
    print(f"Executive Flight: {item['executiveFlight']['riskLevel']}")
    print(f"Key Risks: {item['keyRisks']}")
    print(f"Key Strengths: {item['keyStrengths']}")

JavaScript

import { ApifyClient } from "apify-client";

const client = new ApifyClient({ token: "YOUR_API_TOKEN" });

const run = await client.actor("ryanclinton/talent-intelligence-report").call({
    companyName: "Stripe",
    industry: "fintech",
    region: "United States"
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
for (const item of items) {
    console.log(`${item.entity}: ${item.compositeScore}/100 — ${item.verdict}`);
    console.log(`Talent Velocity: ${item.talentVelocity.growthSignal} (${item.talentVelocity.score})`);
    console.log(`Brain Drain: ${item.brainDrain.drainLevel} (${item.brainDrain.score})`);
    console.log(`Exec Flight: ${item.executiveFlight.riskLevel} (${item.executiveFlight.score})`);
    console.log(`Signals:`, item.allSignals);
}

cURL

# Start the actor run
curl -X POST "https://api.apify.com/v2/acts/ryanclinton~talent-intelligence-report/runs?token=YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "companyName": "Stripe",
    "industry": "fintech",
    "region": "United States"
  }'

# Fetch results (replace DATASET_ID from the run response above)
curl "https://api.apify.com/v2/datasets/DATASET_ID/items?token=YOUR_API_TOKEN&format=json"

How Talent Intelligence Report works

Phase 1 — Parallel data collection across 7 sources

The actor fires all 7 sub-actor calls simultaneously using Promise.allSettled, so total run time is bounded by the slowest single source rather than the sum of all sources. Each sub-actor retrieves up to 1,000 records. Failed sub-actor calls return empty arrays and do not abort the run — scoring proceeds with whatever data is available. The 7 sources are: job-market-intelligence (job postings with title, function, and seniority signals), patent-search (USPTO filings), epo-patent-search (EPO filings), orcid-researcher-search (academic researcher profiles), company-deep-research (news and profile data), github-repo-search (public repositories with stars, contributors, topics), and sec-insider-trading (Form 4 filings with transaction type and value).

Phase 2 — Four independent scoring models

Each model operates on the same collected data object. Talent Velocity extracts job titles and categorizes them into 7 functions (engineering, sales, marketing, product, design, data, other), then applies a Herfindahl-Hirschman Index calculation across those functions — a lower HHI (more evenly distributed hiring) produces a higher diversity score, capped at 25 points. GitHub repositories updated within the last 180 days contribute to technical momentum. Brain Drain compares patent filing timestamps: patents older than 12 months are classified as "older", patents within the last 12 months as "recent". If recent volume is below 50% of older volume, a patent decline flag triggers a 25-point penalty. ORCID researchers with 2 or more affiliations are flagged as mobility risks. Competitive Capability scans all text from job titles, patent titles, and GitHub descriptions and topics against a 10-domain keyword dictionary (e.g., "machine learning", " ai ", "deep learning" → AI/ML domain) and scores by the number of detected domains. Executive Flight Risk parses Form 4 transaction types to separate sales from purchases, calculates the sell ratio, identifies individual executives with 3 or more sell transactions by name, flags sales over $1M as large transactions, and scans company deep research text for keywords including "layoff", "restructur", "lawsuit", and "sec probe".

Phase 3 — Composite scoring and override logic

The weighted composite combines all four models: Talent Velocity × 0.25 + (100 − Brain Drain) × 0.25 + Competitive Capability × 0.30 + (100 − Executive Flight) × 0.20. Brain Drain and Executive Flight are inverted because higher scores in those models represent worse outcomes. After the weighted sum is computed, two hard override rules apply: if Executive Flight riskLevel is CRITICAL or Brain Drain drainLevel is HEMORRHAGING, the verdict is forced to AVOID regardless of the composite score. Verdicts map as: INVEST (70+), MONITOR (50-69), CAUTION (30-49), AVOID (below 30).

Phase 4 — Signal aggregation and report assembly

All signal strings from all four models are merged into allSignals. Key risks are generated from threshold breaches: Brain Drain score ≥ 60, Executive Flight score ≥ 60, Talent Velocity score < 20, Competitive Capability score < 30. Key strengths are generated from threshold achievements: Velocity ≥ 60, Drain < 30, Capability ≥ 60, Flight < 20. The final report includes sample raw data from each source (up to 10-15 records per source) to allow analysts to spot-check signal accuracy.

Tips for best results

1. Use the full legal company name for patents and SEC data. "Alphabet" will return more USPTO and EDGAR records than "Google". Check the company's EDGAR entity name at sec.gov/cgi-bin/browse-edgar before running.
2. Add region when the company name is common. "Oracle" is unambiguous, but "Acme" or "Pinnacle" will pull job data from multiple unrelated companies. Add a city or state to sharpen the job market query.
3. Treat the verdict as a first-pass screen, not a final decision. The scoring models use publicly available signals. Cross-reference CAUTION or AVOID verdicts with primary sources — particularly the named serial sellers in Executive Flight signals — before acting on them.
4. Schedule monthly runs to track score trajectory. A company that moves from INVEST to MONITOR over three quarters is more actionable than a static snapshot. Use Apify's built-in scheduler to automate monthly cadences for every portfolio company.
5. Check dataSources counts when a score looks wrong. If usPatents and euPatents are both 0 for a company known to have IP, the company may be registered under a different legal name. Retry with the subsidiary or holding company name.
6. Combine with Company Deep Research for a narrative summary that contextualizes the quantitative signals in this report.
7. Compare the sellRatio to sector norms. A 0.70 sell ratio at a mature dividend-paying company may reflect routine diversification; the same ratio at a 3-year-old startup is a stronger red flag. Use sector context when interpreting Executive Flight scores.

Combine with other Apify actors

Limitations

• Job market data reflects available postings at query time. Companies that hire primarily through referrals or executive search firms may show artificially low talent velocity scores. The score reflects advertised, not total, hiring activity.
• Patent analysis covers the most recent results returned by USPTO and EPO search APIs. For companies with very large patent portfolios (500+ patents), the 1,000-record limit per sub-actor may truncate older filings, affecting the patent decline calculation in the Brain Drain model.
• ORCID researcher data is self-reported and voluntary. Companies in industries with low academic researcher presence (retail, hospitality, construction) will return few or no ORCID records, producing a default Brain Drain researcher score rather than a measured one.
• SEC insider trading data applies only to publicly traded US companies. Private companies, foreign-listed companies, and subsidiaries without their own SEC registrations will show zero insider transactions, limiting the Executive Flight model to job posting signals only.
• GitHub repository data reflects public repositories only. Companies that develop proprietary software internally with no open-source presence will show lower Competitive Capability scores than their actual engineering depth warrants. Consider this when analyzing enterprise software vendors or defense contractors.
• Technology domain keyword matching is broad. The keyword dictionary assigns domains based on substring matching across job titles, patent titles, and repository descriptions. A company filing patents about "security of cloud storage" will score in both Cybersecurity and Cloud domains, which may overstate breadth.
• Company name disambiguation is not automatic. If companyName matches multiple entities (subsidiaries, joint ventures, competitors with similar names), the actor will aggregate data from all of them. Use the full legal name and add region context to reduce this risk.
• The composite scoring model is calibrated for technology and knowledge-economy companies. Asset-heavy industries (manufacturing, energy, agriculture) typically have lower GitHub presence and fewer ORCID researchers, which structurally deflates Competitive Capability scores relative to tech peers.

Integrations

• Zapier — trigger a talent intelligence report automatically when a new company enters your CRM pipeline and route AVOID verdicts to a separate review queue
• Make — build a multi-step workflow that runs this report, filters by verdict, and creates investment memo drafts in Notion or Google Docs
• Google Sheets — export composite scores and all four dimension scores for a portfolio of companies into a tracking spreadsheet with automatic weekly refresh
• Apify API — embed talent intelligence runs into proprietary investment platforms, fund management systems, or due diligence portals via REST
• Webhooks — fire a Slack notification when a portfolio company's run completes and the verdict changes from the previous month's baseline
• LangChain / LlamaIndex — feed the structured allSignals array and sampleData into a RAG pipeline to generate investment memo sections from analyst queries

Troubleshooting

• All dataSources counts are zero or very low. The company name may not match the canonical form used in job boards, patent databases, or EDGAR. Try the full legal name, try the ticker symbol for public companies, and add an industry or region qualifier. Check the Apify run log for any sub-actor timeout errors.
• Composite score seems unexpectedly low for a well-known company. Most commonly caused by low patent and GitHub counts for companies that operate in non-technical sectors or keep all development proprietary. Check the dataSources counts and read the allSignals array — if signals are sparse, the scoring models have limited data to work with rather than detecting actual weakness.
• Executive Flight score is 0 or very low for a public company. The SEC insider trading sub-actor searches by company name. If the EDGAR entity name differs from the input (e.g., "Alphabet Inc." vs. "Google"), no Form 4 records will be returned. Retry with the exact EDGAR entity name.
• Run is taking longer than 10 minutes. One or more sub-actors may be queuing due to platform load. The actor uses Promise.allSettled with a 120-second timeout per sub-actor call. If a sub-actor exceeds this, it returns an empty array and the run continues. Check the Apify run log for timeout warnings on specific sources.
• Brain drain score is high but the company appears healthy externally. High brain drain scores for private companies often reflect the absence of SEC data (default score contribution) rather than actual measured drain. Check dataSources.insiderTransactions — if it is 0, the insider component is not measured, and the score may be inflated by the default imputation.

Responsible use

• This actor only accesses publicly available data from job boards, patent databases, ORCID, GitHub, and SEC EDGAR filings.
• Patent, ORCID, and SEC data are official public records. GitHub and job posting data is publicly accessible on the open web.
• Do not use this actor's output as the sole basis for employment decisions, individual targeting, or harassment.
• Comply with applicable securities regulations when using insider trading signals for investment decisions. This output does not constitute financial advice.
• For guidance on web scraping legality, see Apify's guide.

FAQ

How many data sources does the talent intelligence report query? The actor queries 7 sources in parallel: job market postings, USPTO patents, EPO patents, ORCID researcher profiles, company deep research, GitHub repositories, and SEC Form 4 insider trading filings. All 7 are queried simultaneously, so run time is bounded by the slowest source rather than their sum.

How does the talent velocity score measure hiring momentum? Talent Velocity scores 0-100 using four components: job posting volume and seniority mix (max 40 points), hiring function diversity measured by the Herfindahl-Hirschman Index — lower concentration = higher score (max 25 points), GitHub repository activity within the last 180 days as a technical momentum proxy (max 20 points), and company growth signals from funding, expansion, and headcount mentions in deep research results (max 15 points).

What SEC data does the executive flight risk model use? The model analyzes Form 4 filings from SEC EDGAR, which disclose transactions by directors, officers, and 10%+ shareholders. It calculates the sell/buy ratio across all transactions, flags individual transactions over $1M, identifies serial sellers with 3 or more sell transactions by name, counts C-suite job openings as replacement hiring signals, and scans company research text for distress keywords including "layoff", "restructuring", "lawsuit", and "sec probe".

Does the talent intelligence report work for private companies? Partially. Job postings, USPTO and EPO patents, ORCID researcher profiles, and GitHub repositories are all available for private companies. SEC Form 4 insider trading data is only available for US-listed public companies, so the Executive Flight dimension will have limited data for private entities, producing a conservative default score rather than a measured one.

How accurate is the brain drain index for detecting talent attrition? The Brain Drain model uses indirect signals: patent output trends, researcher multi-affiliation patterns, and executive equity transactions. It does not access employee departure data directly. It is most reliable for companies with active patent portfolios and publicly traded status. For private companies with no patent history and no EDGAR filings, the model has limited signal and the score should be weighted accordingly.

How is this different from LinkedIn Talent Insights or PitchBook? LinkedIn Talent Insights and PitchBook are subscription tools costing $599-$24,000+ per year. This actor provides on-demand reports at approximately $0.35 per company with no subscription commitment, synthesizes patent, GitHub, and SEC data that neither LinkedIn nor PitchBook covers in one place, and returns structured JSON that integrates directly into investment workflows via API. The trade-off is that LinkedIn has more granular individual employee-level data, which this actor does not access.

Can I run talent intelligence reports on multiple companies at once? You can trigger multiple runs in parallel via the API or schedule sequential runs. Each run is one company. For batch comparisons, run all companies with the same industry and region settings to ensure the composite scores are comparable, then sort by compositeScore to rank the cohort.

How long does a typical talent intelligence report run take? Most runs complete in 2-5 minutes. The bottleneck is the slowest of the 7 parallel sub-actor calls. Companies with large patent portfolios or many job postings may take up to 10 minutes. Each sub-actor call has a 120-second timeout; if a source exceeds this, it returns an empty array and scoring continues with available data.

Is it legal to use SEC insider trading data for investment analysis? SEC Form 4 filings are public records published by the SEC specifically to provide market transparency. Analyzing them is legal and is standard practice in institutional investment research. This actor aggregates publicly disclosed information. It does not provide investment advice. Consult a licensed financial advisor before making investment decisions based on any data analysis output.

Can I schedule this actor to run monthly portfolio scans automatically? Yes. Apify's built-in scheduler lets you run the actor on a daily, weekly, or custom cron schedule. To monitor a portfolio of companies, create one scheduled run per company or trigger batch runs via the API. Use webhooks to alert your team when a company's verdict changes from one run to the next.

What happens if one of the 7 data sources fails during a run? The actor uses Promise.allSettled to collect results from all 7 sources. If any individual source fails or times out, its result is replaced with an empty array and the run continues. The scoring models proceed with whatever data is available. Check dataSources in the output to see which sources returned records — a zero count indicates that source either had no data or encountered an error.

How do I interpret a CAUTION verdict for a company I know is healthy? A CAUTION verdict (composite score 30-49) often reflects limited public data rather than genuine organizational risk. Check the dataSources counts: if multiple sources returned 0 records, the score reflects data absence rather than detected problems. Companies that hire privately, file no patents, operate outside the US, or keep all development proprietary will structurally score lower than their actual health warrants.

Help us improve

If you encounter issues, you can help us debug faster by enabling run sharing in your Apify account:

1. Go to Account Settings > Privacy
2. Enable Share runs with public Actor creators

This lets us see your run details when something goes wrong, so we can fix issues faster. Your data is only visible to the actor developer, not publicly.

Support

Found a bug or have a feature request? Open an issue in the Issues tab on this actor's page. For custom solutions or enterprise integrations, reach out through the Apify platform.