Scout — OSINT Lead Enrichment, KYC Screening, Domain Intelligence (pay per result)

One actor for B2B lead enrichment, KYC / AML screening, vendor due diligence, recruiter sourcing, creator research, and competitive intelligence. Hand Scout an email, name, domain, or handle — pick a strategy that matches your use case — get back a verified, audit-ready dossier with provenance on every fact. No API keys, no per-seat pricing, no subscriptions. You pay only when Scout delivers a result.

$0.05 per person · $0.15 per organization · $0.02 per teammate auto-discovered. Apify compute on top.

Scout is a credential-free OSINT actor that turns any identity fragment — email, full_name, domain, linkedin_url, github_username — into a structured dossier with sanctions screening, breach exposure, work-email discovery, cross-platform identity correlation across 3,000+ websites, document mining (PDF / OCR / NER), and (for org strategies) the company's full leadership team enriched as related dossiers in the same run.

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Pick a strategy

The single most important input is strategy. Scout ships 6 canonical strategies that cover the common B2B / KYC / OSINT use cases, with sensible defaults for effort, team-spawn behavior, and which enrichment sources fire.

Strategy	What it's for	Entity	Default effort	Spawns team
quick_verify	"Is this email / handle real?" — fast identity validation	person	1	no
enrich_person	Find a person — work email, LinkedIn, employer, GitHub, social posts. Default for sales prospecting and recruiter sourcing.	person	2	no
enrich_org	Map an organization — domain + tech stack + sitemap + blog activity + funding events + leadership team. Default for vendor risk, competitive research, and "should I work here."	org	2	up to 8
compliance	KYC / AML — global sanctions + PEP + adverse news + breach exposure. Auto-detects person vs org from input shape.	either	3	no
creator_research	Influencer / creator audit — cross-platform audience, post streams, follower counts, recent topics	person	2	no
deep_dive	Full online snapshot — every applicable enricher fires (Internet Archive history, ~3,000-site username sweep, Instagram + Twitter + Reddit post streams, dev.to / Medium / Hacker News / Bluesky / Mastodon / Keybase, OpenAlex + arXiv, breach + sanctions, EXIF avatar mining, typosquat detection on personal domain). Slowest + most thorough.	either	3	up to 5

Legacy aliases (still accepted)

The actor previously shipped 11 strategies. The five organization-specific names plus prospecting / recruiter / due_diligence / org_compliance continue to work — they resolve to one of the canonical six with a small tag overlay so existing customer integrations don't break.

Legacy name	Resolves to	Overlay
prospecting	enrich_person	(no change)
recruiter	enrich_person	+ academic / documents tags
due_diligence	enrich_person	+ compliance / academic / documents, default effort 3
vendor_dd	enrich_org	+ compliance / phishing tags, default effort 3, spawn cap 5
competitive_research	enrich_org	spawn cap 10
hiring_target	enrich_org	spawn cap 5
org_compliance	compliance	(no change — compliance auto-detects entity kind)

New integrations should use the canonical names; legacy aliases will continue to work for the foreseeable future.

Try it in 30 seconds

// B2B prospecting / recruiter sourcing on a person
{ "strategy": "enrich_person", "full_name": "Jane Doe", "company_name": "Acme Inc" }

// KYC / AML screen — auto-detects person vs org from the input
{ "strategy": "compliance", "full_name": "Jane Doe", "email": "jane@acme.com" }
{ "strategy": "compliance", "domain": "acme.com" }

// Map an organization (vendor risk / competitive research / "should I work here")
{ "strategy": "enrich_org", "domain": "acme.com" }

// Full online snapshot of a person — every applicable source
{ "strategy": "deep_dive", "full_name": "Jane Doe", "email": "jane@acme.com" }

The effort dial (1-3) modulates depth within a strategy: 1 is fast verify-only, 2 is the standard run, 3 is thorough (more candidate sources, bigger team-spawn cap, OCR on documents). Per-result billing is identical at every effort level.

Advanced: pass-count override

Each strategy ships with a default number of orchestration passes (1 for quick_verify, 2 for most, 3 for deep_dive). A pass = one full sweep of every eligible enricher. Late-arriving evidence in pass N can unlock new enrichers in pass N+1 (e.g. a homepage scrape reveals a GitHub URL → next pass runs the GitHub stack).

Override with the optional passes input (range 1-4):

// Re-check a known-good seed cheaply
{ "strategy": "enrich_person", "email": "jane@acme.com", "passes": 1 }

// Chase deeply-chained evidence on a hard case
{ "strategy": "deep_dive", "email": "jane@acme.com", "passes": 4 }

The orchestrator stops early when no new evidence arrives between passes, so raising the cap rarely wastes work — most runs converge in 2 passes regardless.

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What Scout returns

Each input becomes one or more dataset records. The primary dossier carries every populated Field with a typed verdict (best-guess value with confidence + sources) plus the full evidences chain so every fact is traceable. Spawned related dossiers (teammates discovered via team-page scraping or GitHub org membership) land as separate dataset records.

Identity (every strategy)

• Name parsing — full_name → first / last + name-commonness rating (rare / common / very_common, region-aware via global names dataset across 150 countries) so disambiguation strictness scales with name ambiguity
• Email validation — syntax + MX record + breach lookup (Have I Been Pwned)
• Phone normalization — any format → E.164
• Personal-domain detection — auto-probes <first><last>.{com,io,co,me,…} against the lead's name

Email-keyed enrichment

• Gravatar profile lookup by email-MD5 hash
• Account footprint scan across 120+ services (account-existence probing via password-reset endpoints)
• Recovery email + phone hints — Adobe / Samsung / mail.ru / Odnoklassniki sometimes leak partially-redacted recovery identifiers in their password-reset response, fed back into the dossier
• Email pattern detection — when the lead's email matches a known shape, infer the employer's pattern (first.last, flast, firstl, …) for downstream prospecting
• SMTP RCPT verification with explicit catch-all domain detection

Cross-platform identity

• 3,000+ sites probed via cross-platform username lookup. Surfaces GitHub, LinkedIn, Twitter/X, Bluesky, Mastodon, Reddit, Stack Overflow, Medium, dev.to, Hacker News, Wikipedia, Patreon, Substack, Twitch, Steam, Spotify, Behance, Dribbble, DeviantArt, Goodreads, Letterboxd, Strava, Chess.com, Vivino, Etsy, Trello, Foursquare, Flickr, Imgur, Wattpad, Pinterest, TikTok, Instagram (where public), Snapchat, Tumblr, and many more
• Keybase profile — cryptographically-verified cross-platform proofs
• Schema.org structured-data extraction from any profile page found
• Known-handle disambiguation — when multiple matches surface, the GitHub name field cross-checks against the lead's name to demote impostors

GitHub depth (enrich_person at effort 2-3)

• Profile, top languages, follower count, public_repos
• Recent activity timeline + commit cadence + timezone inference
• Top repos with stars / topics / language
• Email harvest from public commits
• Package ownership across npm / PyPI / RubyGems / crates.io / Docker Hub

Compliance (compliance strategy + deep_dive)

• Global sanctions + PEP screening across UN / EU / UK / Switzerland / US OFAC sanctions + PEP lists + crime / terror / corruption — ~600,000 consolidated entities
• Token-overlap sanity check kills hash-collision false positives so a name like "Anmol Sharma" doesn't match against unrelated entities
• Adverse news scan with anchor-coherent snippet filtering — drops hits that name a different identifier than the primary anchors
• Breach report for the email's domain via public breach-aggregation service
• Held-back candidates — surfaces same-name profiles Scout considered but didn't promote, with the distinguishing attributes that disqualified them. Transparent disambiguation, not silent filtering.

Academic (enrich_person at effort 3, deep_dive)

• Author + works + citation graph — ~250M papers, ~250M authors, ORCID-aware disambiguation
• Preprint search by author across the major preprint servers
• Top-cited works, affiliations, h-index proxies

Document mining

• Open-web search across 12 fallback engines (Brave, Google, Yandex, Startpage, Yep, DuckDuckGo, Bing, Ecosia, Yahoo, Mojeek, Qwant, SearXNG) with TLS-fingerprint impersonation
• PDF + DOCX extraction with table-aware text recovery → emails / phones / URLs / employers / skills / education
• Named-entity co-occurrence finds people, companies, locations mentioned alongside the lead with proximity scoring
• Resume parsing — section-aware → structured WorkExperience and EducationEntry
• Image mining with EXIF, alt-text, OCR

Domain enrichment (org strategies)

• WHOIS + DNS (A / AAAA / MX / NS / TXT) + IP geolocation + ASN
• MX provider detection (Google Workspace / Microsoft 365 / ProtonMail / Zoho / Fastmail / SES / Mailgun / SendGrid)
• CDN + tech-stack fingerprinting with a 3,000-pattern technology database
• Subdomain enumeration via certificate transparency logs
• Phishing / typosquat detection via domain-permutation + DNS resolution
• Sitemap categorization — /sitemap.xml URLs bucketed by intent (pricing, products, blog/news, customers / case studies, careers, docs, legal) so the customer sees product-shape signal, not a raw URL list
• Well-known endpoints + favicon hash + WordPress REST user enum (when the org runs WP)
• JSON-LD projection — Schema.org/Organization blocks parsed and projected onto first-class slots: description (one-line "what they do"), location (HQ from address / contactPoint.areaServed), profiles (each sameAs URL becomes a service-tagged profile entry — LinkedIn, Twitter, GitHub org, Crunchbase, Facebook), inline Person schemas spawn related teammate dossiers with jobTitle
• Apify Proxy honoured everywhere — no upstream source bypasses your residential proxy

Hiring signals (enrich_org)

• ATS detection — Greenhouse / Lever / Ashby / Workable APIs probed for open roles + departments
• Team-page extraction — fetches team / about / leadership pages with a real browser, extracts names + titles via named-entity recognition, spawns one Person dossier per teammate which gets the full enrichment pipeline (work-email finder targeted at the company domain, GitHub, social profiles, sanctions, …)
• Title extraction from snippets — match across all collected dork hits catch role titles (CEO / CTO / Chief Technology Officer / VP / Head of …) even when never listed on a team page
• Internet Archive history — historical snapshots of pages that auth-wall today
• Crunchbase / Glassdoor / BuiltWith / Product Hunt / OSS-repo signals

Press, funding & company timeline (org strategies)

• Funding events — news-targeted dorks (<org> raised | "series a" | seed | funding) surface investment announcements; named-entity recognition pulls money amounts + funding-keyword proximity extracts the figure ($200K, $5M Series A)
• Launch / acquisition / partnership detection — headline patterns (launched, acquired, partners with, appoints) classify each press hit into an event kind (funding / acquisition / launch / partnership / hiring)
• Founding date inference — founded / since / established keyword anchors + date entity recognition → org's founding year
• Headcount inference — regex matches on team of N, N+ employees, N-person team patterns across the snippet corpus
• HQ / location inference — top geopolitical entity (city / country) by frequency across all dork hits, useful when the org doesn't publish an address

Creator-economy signals (creator_research)

• Twitter / X post stream (recent posts + engagement)
• Reddit post + comment history
• Instagram public profile (followers, post count, business-account flag, external URL)
• YouTube + TikTok + Twitch + Patreon profile detection via username pivot

Quality + transparency

• Lead score (0-100) with persona classification (developer / executive / academic / creator)
• Identity-locked flag — true when ≥2 of {email, full_name, github, linkedin} clear the verdict threshold
• Per-field confidence map + per-field source list
• Coherence filter — discoveries whose context bag contradicts primary anchors get demoted to candidates with a clear reason (rather than silently merged or dropped)

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Output shape

Each dataset record holds a primary dossier + run metadata + (optionally) markdown:

{
  "primary": {
    "id": "lead-1",
    "kind": "person",                 // or "organization"
    "full_name": {
      "verdict":   { "value": "Jane Doe", "confidence": 0.95, "sources": ["github_profile","gravatar","input"] },
      "evidences": [ /* full chain with source, timestamp, derived_from, … */ ]
    },
    "email":      { "verdict": [ /* multi-card */ ], "evidences": [ /* … */ ] },
    "linkedin_url": { "verdict": { /* … */ }, "evidences": [ /* … */ ] },
    "title":      { "verdict": { "value": "CTO", "sources": ["snippet_extract"] }, "evidences": [ /* … */ ] },
    "location":   { "verdict": { "value": "Waterloo", "sources": ["snippet_extract","page_meta"] }, "evidences": [ /* … */ ] },
    "handles":    { "verdict": [ /* … */ ], "evidences": [ /* … */ ] },
    "profiles":   { "verdict": [ /* … */ ], "evidences": [ /* … */ ] },
    "documents":  { "verdict": [ /* … */ ], "evidences": [ /* … */ ] },
    "sanctions":  { /* … */ },
    "adverse_news": { /* … */ },
    "account_footprint": { /* … */ },
    // Org-only slots (kind=="organization")
    "description":{ "verdict": { "value": "Voice AI for home services …", "sources": ["page_meta","homepage"] } },
    "founded":    { "verdict": { "value": "2023", "sources": ["snippet_extract"] } },
    "headcount":  { "verdict": { "value": "50", "sources": ["snippet_extract"] } },
    "events":     { "verdict": [ { "value": { "kind": "funding", "amount": "$200K", "url": "…" } } ], "evidences": [ /* … */ ] },
    "tech_stack": { "verdict": [ /* multi-card */ ] },
    "candidates": { /* held-back same-name profiles */ },
    "related":    [ /* recursively-shaped child dossiers — spawned teammates */ ]
  },
  "quality": {
    "score": 72, "tier": "hot", "persona": "developer",
    "identity_locked": true, "evidence_strength": "verified",
    "confidence": 0.85, "rationale": ["identity locked: 4/4 core fields ≥0.7"]
  },
  "_meta": {
    "kind": "person", "record_kind": "primary", "strategy": "enrich_person"
  }
}

Set outputFormat: "md" to get a clean markdown rendering instead — drop directly into a Claude / GPT prompt for outreach copy, qualification, summarization. Set outputFormat: "both" to keep both.

The Apify dashboard ships 5 pre-built table views: Overview (default), Compliance, Organization, Social presence, Provenance — filter by strategy, kind, score tier, or identity_locked without writing a transformation.

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Pricing

Pay-per-event Apify monetization. No monthly fee, no per-seat license, no API keys.

Event	Fires when	Price
person_enriched	A person dossier completes (primary or seeded-from-email)	$0.05
domain_enriched	An organization / domain dossier completes	$0.15
team_member_spawned	A teammate found via org → team discovery and enriched	$0.02 each
apify-actor-start	Fixed start fee	$0.00005

Typical runs:

• enrich_person on a person → $0.05
• compliance on a person (sanctions + adverse news + breach) → $0.05
• enrich_org on a domain returning 5 enriched leadership teammates → $0.15 + 5 × $0.02 = $0.25
• enrich_org on a domain returning 8 teammates → $0.15 + 8 × $0.02 = $0.31
• 1,000 cold leads at enrich_person strategy, 70% identity-lock rate → **$35 + Apify compute**

Apify compute fees are billed separately by Apify based on memory + duration — typically a few cents per run.

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How Scout compares

	Scout	Apollo / ZoomInfo / Clearbit	Hunter.io	OSINT Industries	SpiderFoot HX
Pricing	Pay-per-result (5¢–15¢)	$1K–$30K+ / yr per seat	$50–$500+ / mo	Subscription	$99+ / mo
API keys / minimums	None	Yes	Yes	Yes	Self-hosted only
Data source	Public web with per-fact provenance	Proprietary database	Email guesses	Public + private mix	Public + paid feeds
Sanctions / KYC / AML	✅ Global sanctions + PEP + OFAC	Add-on	No	Yes	Yes
Email finder + SMTP verify	✅ With catch-all detection	Database	✅	No	No
Domain → team people	✅ Spawn each + enrich in same run	Database	No	No	Limited
Cross-platform identity (3000+ sites)	✅	None	No	Limited	Yes
Resume / PDF / OCR mining	✅ Table-aware + named-entity extraction	Limited	No	No	Limited
GitHub depth	✅ Profile, repos, packages, commits	None	No	Limited	Limited
Academic papers / citations	✅ ~250M papers, ORCID-aware	No	No	No	No
Adverse media	✅ Anchor-coherent filtering	Add-on	No	Yes	Yes
Tech stack detection	✅ 3000-pattern fingerprinting	Limited	No	No	Yes
Phishing / typosquat detection	✅	No	No	No	Yes
Auditable provenance per fact	✅ Full evidence chain	No	No	Mixed	Mixed
Self-hostable	✅ Run on your own Apify account	No	No	No	Yes

Scout isn't a 200M-row contact database. It's a verifiable, one-shot dossier per lead, paid by the result, with provenance your compliance team can audit.

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Use cases (with strategy mapping)

B2B Lead Enrichment for Sales Prospecting (Apollo / Clearbit Alternative) — enrich_person

Turn a sparse CRM record into a complete contact dossier. Pass full_name + company_name and Scout finds the work email via SMTP verification with catch-all detection, the LinkedIn URL, GitHub presence, social profiles, and the lead's role. Pay-per-lead pricing instead of per-seat licenses.

Sales Intelligence at the Organization Level (ZoomInfo Alternative) — enrich_org

Pass a domain, get the leadership team. Scout walks team / about / leadership pages, extracts names + titles via named-entity recognition, spawns a Person dossier per teammate, and enriches each — outreach-ready in one run. One billable input → org dossier + N enriched team members.

KYC / AML Compliance Screening (OFAC + PEP + Sanctions) — compliance

Sanctions screen against a global consolidated dataset (UN / EU / UK / Switzerland / OFAC / PEP — ~600K entities), adverse-media scan with anchor-coherent filtering (drops false positives that name a different identifier), breach exposure check. Auto-detects person vs organization from the input — email/name → person, bare domain → org. Compliance-ready output with full provenance for every flag.

Vendor Due Diligence & Procurement Risk Assessment — enrich_org (or legacy vendor_dd)

Procurement risk scan on a vendor: WHOIS history + tech stack + ATS hiring activity + sanctions on the org + leadership team enriched + phishing-domain detection. The legacy vendor_dd alias adds compliance + phishing tags and bumps default effort to 3; the canonical enrich_org at effort 3 plus a separate compliance run gives the same coverage with explicit provenance.

Recruiter Sourcing & Candidate Research (Hired / SourceWhale Alternative) — enrich_person (or legacy recruiter)

Enrich a candidate's full public footprint: GitHub depth (repos, languages, commit cadence), Stack Overflow reputation, package ownership across npm / PyPI / RubyGems / crates.io, academic papers + citations + co-authors, structured employment history mined from PDF resumes. The legacy recruiter alias adds academic + documents tags on top of the base enrich_person set.

Investment Due Diligence on People (Background Check Alternative) — enrich_person at effort 3 (or deep_dive)

Person-level DD: identity verification + compliance + academic record + professional history + cross-platform consistency check. Surfaces inconsistencies (different email in public records vs. CV vs. LinkedIn) as held-back candidates rather than silently merging. The legacy due_diligence alias is enrich_person + compliance / academic / documents at effort 3.

Influencer & Creator Research / Sponsorship Vetting — creator_research

Cross-platform audience audit: Twitter/X post stream + Reddit history + Instagram metrics + YouTube/TikTok/Twitch detection. Useful for sponsorship matching, brand-fit qualification, audience-overlap analysis, fake-follower screening.

Competitive Intelligence & Competitor Mapping — enrich_org (or legacy competitive_research)

Map a competitor's footprint: tech-stack fingerprinting, hiring signals from major ATS platforms (Greenhouse / Lever / Ashby / Workable), GitHub org activity, open-source repo detection, leadership team mapping, Internet Archive timeline of their site, blog activity heartbeat, recent funding / launch / acquisition events.

Pre-Offer Company Research ("Should I Work Here?") — enrich_org (or legacy hiring_target)

Check an org before accepting an offer: Glassdoor + Crunchbase signals, hiring momentum (ATS open roles + departments), tech stack, leadership profiles, OSS commitment, growth trajectory.

CRM Hygiene, Dedup & Cold-List Scoring

Re-enrich existing leads, surface stale records, dedupe via cross-source identity verification + the coherence filter. Run at quick_verify strategy for ~10s/lead cold-list scoring before targeting outreach.

OSINT, Investigative Journalism & Background Research

Public-source person lookup with per-field provenance. Cross-platform identity correlation surfaces aliases. Document mining on web-search results pulls structured evidence from PDFs. Audit-ready output for editorial standards.

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FAQ

How do I find someone's work email from just their name? Pass strategy: "enrich_person" with full_name + company_name (or domain). Scout runs domain enrichment, generates plausible work-email patterns (first.last, flast, firstl, f.last, first, …), runs SMTP RCPT against each candidate, and returns the first verified hit with full provenance. Catch-all domains are detected explicitly and flagged so you don't act on false positives.

What sanctions / PEP coverage do I get? The compliance strategy screens against a consolidated global dataset of ~600,000 sanctioned / PEP / criminal-listed entities aggregated from UN, EU, UK, Switzerland, US OFAC, PEP databases, and crime / terror / corruption lists. Token-overlap sanity discards hash-collision false positives so a name like "Anmol Sharma" doesn't get matched against "SHAZAND PETROCHEMICAL COMPANY." Auto-detects person vs organization from the input shape.

How does the org → team flow work? At the enrich_org strategy, Scout enriches the domain (WHOIS / DNS / hosting / tech-stack fingerprinting / certificate transparency / sitemap) AND fetches the team / about / leadership pages with a real browser so JavaScript-rendered content lands in the DOM. Named-entity extraction surfaces names with strict validity filtering (rejecting marketing copy and section headings), pairs each detected name with the title text immediately following it, and spawns one Person dossier per teammate. Each spawned person is enriched concurrently with the primary's later tiers — so a single domain input returns the org + N enriched team members in roughly the same wall-clock time as enriching just the org.

How does pay-per-event billing work? Apify's pay-per-event meter ticks once per dataset record pushed. Each primary dossier fires person_enriched ($0.05) or domain_enriched ($0.15) depending on _meta.kind. Each spawned related dossier fires team_member_spawned ($0.02). If Scout doesn't push a primary record (e.g., input was empty or the actor crashed before completion), nothing bills.

Can I run on a list of 10,000 leads? Yes — invoke the actor once per lead via the Apify API. Apify scales horizontally. Budget ~30s per person at prospecting effort=2, ~90s per domain at vendor_dd effort=3 (with team spawn). Run cold-list scoring at quick_verify (~10s) to keep wall time tight; bump to higher strategies for the leads that actually convert.

Why don't I see LinkedIn personal-profile content even though the URL is in the output? LinkedIn aggressively gates personal profiles. Scout records the URL it found but won't fake the content if LinkedIn returns 999. Company pages frequently come through; personal pages typically don't, and we tell you that openly rather than emit dead fields.

Does Scout work for non-English names? Yes. The commonness enricher is region-aware (backed by a global names dataset across 150 countries), so Anmol Sharma is correctly scored as common-in-India rather than rare-globally. Romanized names work best; CJK names work but disambiguation is harder when the input is name-only.

Will I be charged for a thin run? The quality block on every record includes a tier field. If you only want to bill on identity-locked outputs, filter the dataset by quality.identity_locked = true before downstream processing — Apify's pay-per-event meter has already counted the record either way (we publish what we find), but your downstream code can ignore thin records.

How is Scout different from a 200M-row contact database? Apollo / ZoomInfo / Clearbit ship a database snapshot — you query their data. Scout ships a researcher — you give it a clue, it goes to the public web and brings back a dossier with proof. Different shape, different price model. Scout is best when you need current, verifiable, low-volume enrichment with provenance. The big DBs are still better when you need 50K records overnight and don't care about audit trail.

Can I see the output before paying? Yes — Scout is free to install on Apify and you only pay when the per-event billing fires. Run it on a single lead from your CRM at quick_verify strategy to evaluate the output shape and quality before scaling up.

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Limitations

• Anti-bot variability. LinkedIn personal profiles, Glassdoor company pages, Crunchbase profile bodies, and a handful of aggressive anti-bot sites still 999 / 403 even with stealth + proxy. Scout records what it tried and never falsifies the result.
• Identity ambiguity on common names. A common first + last name combination with no email, handle, or domain is hard to disambiguate. Scout's commonness enricher flags the risk and the coherence filter requires multi-anchor corroboration before promoting a candidate to a primary identity field.
• SMTP RCPT is unreliable in production. Many providers accept-all or block from cloud IPs. A successful SMTP RCPT means "the receiving server didn't reject" — not "this address truly works." Scout detects catch-all domains explicitly and downgrades the confidence on those hits with a warning in the evidence chain.
• NER false positives on team pages. Entity extractors occasionally tag marketing copy as PERSON. Scout's post-extraction filter (regex blocklists + names-dataset surname validation) catches the vast majority; rare edge cases may slip through.
• Public data only. No customer-supplied API keys, no paid data brokers, no auth-walled content.

Scout never raises on a single source failure — you always get a result, with the gaps clearly marked.

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Use responsibly

The output describes real people and organizations. Your jurisdiction may regulate how this kind of data can be stored, redistributed, or sold (GDPR, CCPA, PIPEDA, state data-broker statutes, etc.). Scout is a research tool — operating it for due diligence, sales research, recruiting, or investigative work on parties with whom you have a legitimate interest is what it's built for. Bulk dataset construction or resale without lawful basis is on you.

Scout never bypasses authentication, never solves CAPTCHAs, and never exceeds public rate-limit guidance. If a source returns "auth required" or rate-limits the request, the corresponding field is left null and the failure is recorded in the evidence chain.

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Run it now

Run Scout on Apify → · Free to install, pay only per delivered result. No subscription, no API keys, no minimums.

Tags: lead enrichment · OSINT · email verification · SMTP verifier · sanctions screening · KYC · AML · OFAC · PEP screening · adverse media · domain intelligence · company enrichment · firmographics · tech stack detection · cross-platform identity · person lookup · contact finder · LinkedIn enrichment · GitHub OSINT · academic papers · citation graph · resume parsing · PDF mining · OCR · breach exposure · vendor due diligence · competitive intelligence · recruiter sourcing · creator research · pay per result · Apollo alternative · Clearbit alternative · Hunter.io alternative · ZoomInfo alternative · OSINT Industries alternative · SpiderFoot alternative