Company Signal Stack — Firmographics + Jobs + Funding + Headcount

Turn a list of company domains into one enriched row per company, joining four public signals that no single enrichment tool ships together:

Signal	What you get	Source
Firmographics + tech stack	name, description, social profiles, detected technologies	company website
Open jobs (hiring signal) ⭐	open-role count, breakdown by department, 30-day hiring velocity, top locations, sampled roles	Greenhouse / Lever / Ashby public APIs
Funding	recent SEC Form D private-placement filings → recently_funded flag + date	SEC EDGAR
Headcount	employee-count snapshot, founded year, HQ, industry	LinkedIn company page (no login)

Plus a derived growth_signals block — a structured buying-intent summary (hiring band, recently_funded, is_growing) you can filter on directly in a CRM, a Clay table, or an AI agent. No prose reports — structured data you pipe.

Why this exists

Firmographic tools (Clearbit/Apollo-style) tell you what a company is. They do not tell you whether it is growing right now — and growth is what makes a company worth a sales rep's time. Company Signal Stack adds the missing buying-intent layer: a company aggressively hiring engineers and fresh off a funding round is a hot account; the same firmographics with zero open roles is cold. We join the hiring signal, the funding signal and the headcount snapshot to the firmographics in one call.

Input

{
  "domains": ["stripe.com", "vercel.com"],
  "includeJobs": true,
  "includeFunding": true,
  "includeHeadcount": true,
  "maxJobsSample": 25,
  "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": ["RESIDENTIAL"] }
}

Field	Default	Notes
domains	–	Company domains or URLs (one row per company)
includeJobs	true	Detect ATS, return open-role signal
includeFunding	true	SEC Form D lookup (US companies)
includeHeadcount	true	LinkedIn employee-count snapshot
maxJobsSample	25	How many individual roles to include (count/breakdown always reflect all roles)
maxCompanies	200	Safety cap per run

Output (one row per company)

{
  "domain": "stripe.com",
  "company_name": "Stripe",
  "company_description": "Financial infrastructure to grow your revenue …",
  "technologies": ["Next.js", "React", "Stripe", "Cloudflare"],
  "tech_count": 4,
  "social_profiles": { "linkedin": "…", "twitter": "…", "github": "…" },
  "linkedin_url": "https://www.linkedin.com/company/stripe/",
  "ats_provider": "greenhouse",
  "open_jobs_count": 312,
  "jobs_by_department": { "Engineering": 140, "Sales": 60, "…": 0 },
  "hiring_velocity_30d": 47,
  "top_locations": ["Remote", "San Francisco", "Dublin"],
  "jobs_sample": [{ "title": "Backend Engineer", "department": "Engineering", "location": "Remote", "url": "…", "posted_at": "2026-06-01" }],
  "recently_funded": true,
  "last_funding_date": "2025-03-12",
  "funding_filings_count": 3,
  "employee_count": 8000,
  "employee_count_basis": "linkedin-snapshot",
  "founded_year": 2010,
  "hq_location": "San Francisco, US",
  "industry": "Financial Services",
  "growth_signals": { "hiring": "high", "open_jobs": 312, "recently_funded": true, "is_growing": true },
  "sources_succeeded": ["website", "jobs", "funding", "headcount"],
  "parse_confidence": 0.95,
  "warnings": []
}

Every record carries the same fixed key set. A leg that finds nothing lowers parse_confidence and is named in warnings — the row is never dropped, so a run over 1,000 domains always returns 1,000 rows you can join on.

Notes on the data

• Headcount is a point-in-time snapshot, not a trend. To track headcount over time, schedule the actor and diff employee_count across runs. We never fabricate a trend field; employee_count_basis always states the source.
• Funding is best-effort and US-centric (SEC Form D). Non-US or unfiled companies simply return recently_funded: null — not an error.
• Open jobs is read from the company's own ATS public job board (Greenhouse, Lever, Ashby). Companies on other systems return ats_provider: null, open_jobs_count: 0.

Pricing

Pay per company enriched (PPE). The run executes on your Apify account, so you pay only platform usage plus the per-result fee — no subscription, no minimums.

Usage statistics

This Actor creates a small, content-free summary at the end of each run. It is used only to monitor reliability and improve this Actor. A copy is saved as USAGE_STATS in your own Apify key-value store, so you can see the exact record created for your run.

Set disableUsageStats to true in the input to opt out. Nothing is sent then; your USAGE_STATS record only says that statistics were disabled.

Only these fields are recorded:

• schema version, Actor name and build number;
• UTC start and finish hour (not a precise timestamp);
• run duration, number of results and time to the first result, each as a coarse range;
• whether the result was empty, the end status, and an error type from a fixed list;
• memory setting and counts of charged events;
• names of the input fields you set, never their values;
• the selected option for input fields that offer a fixed list of choices (for example a sort order).

We do not collect input text, search terms, URLs, domains, usernames, email addresses, names, proxy credentials, tokens, scraped records, output items, raw error messages, stack traces, or hashes of any of those values. Records are kept for no longer than 13 months, used only as aggregated operational statistics, and never sold or shared.

Additional fields (Phase 2)

This Actor also records your Apify user ID, whether Apify marks the account as paying, the size range of list inputs, the selected country when the input offers a fixed list of countries, and one category from a fixed Actor taxonomy. We use these fields only for aggregate reliability, repeat-use and cross-Actor analysis; reports suppress any cell with fewer than five distinct users.

The same disableUsageStats: true input flag turns these fields off too. The user ID is removed after 13 months; we do not export, sell, share, or attempt to re-identify this data.

Run-outcome signals (v2)

To learn whether a run did what it was asked to do, the record also holds a few more coarse ranges and yes/no flags. None of them contains content:

• the result limit you asked for (a range, when the input has one) and what share of it was delivered;
• results delivered per input item you listed (a range);
• output quality as ranges: how fully the result fields were filled, the share of rows that look like errors, the share of duplicate rows, and how many different fields appeared. These are counted in memory while results are saved; no result content is kept;
• how the run was started (console, API, schedule, webhook, another Actor);
• how it ended: stopped by you, timed out, reached the requested limit, stopped by the charge limit, and how many times the platform moved the run;
• if this Actor reports it: how many items to process worked or failed (ranges) and one failure reason from a fixed list;
• a short code made from the names of the input fields you set, never their values.

Repeat-run fingerprint (v2)

When your Apify user ID is recorded (see above), the record also holds an 8-character one-way code made from your input (proxy settings left out) and this Actor's name. It only lets us see that the same account ran the same input again soon after an unsatisfying run; we never see the input itself. It is stored only in the database, never published, and reports use it in aggregate with the same five-user minimum. It is the one exception to the statement above that no hashes are collected, and disableUsageStats: true turns it off.