Alumni signal: 6,140 ex-Big Tech founders and C-levels from a people database, with Apify and Claude Code

Part of the series "GTM workflows with Apify and Claude". The series opener explains the stack and the four ways to run everything below.

Some lists are wanted by half a dozen unrelated readers at once. People who left a large technology company recently and are now building something is one of them. A seed investor wants them before the round is announced. A startup bank or a corporate-card company wants them the week they incorporate. Cloud providers and developer-tool vendors run startup programs for exactly this person. Incorporation, accounting and immigration services want them in month one. Recruiters want them because the first engineering hires happen before a job ad exists. And if you are an engineer looking for a founding role, you want the same list for the same reason.

What these readers have in common is that they usually get this list late, from announcements. Here is the list built from career data on 2026-10-10, in full, with the LinkedIn People Search Actor on Apify. I use it because "used to work at these ten companies, left after this year, now holds this seniority" is three input fields, and the count comes back before any row does, so the whole segment can be pulled instead of a sample.

The signal as a filter

{
  "mode": "count",
  "countries": ["us", "gb", "de", "nl", "ca"],
  "pastEmployerDomains": ["google.com", "meta.com", "amazon.com", "microsoft.com", "apple.com",
                          "salesforce.com", "oracle.com", "cisco.com", "ibm.com", "intel.com"],
  "leftPastEmployerAfterYear": 2025,
  "seniority": ["founder", "cxo"]
}

6,140 people. The Actor adds one thing on its own, visible in the _input echo on the result: people still employed at the ten companies are excluded, so an "Amazon, then Amazon again" promotion does not count as leaving.

For context I ran the wider population as segments in the same call family, which returns counts only: everyone who left those ten companies in 2025 or later and lives in the five countries is 115,137 people. Of those, 26,583 now work at companies of 500 people or fewer, 8,796 of them as managers or above, and 30,840 show no current position at all. The founder and C-level cut is the 6,140 this article is about.

Ten more variants, counts only, to understand what is inside before pulling rows:

Segment	People
Founders and C-levels, as above	6,140
Career started in 2022 or earlier (drops people whose Big Tech stint was an internship or a first job)	5,962
Career started in 2020 or earlier	5,618
And no "student" or "aspiring" in the headline	5,748
Career 2022 or earlier, current role started in 2025 or later	4,125
Career 2022 or earlier, company of 50 or fewer people (or size unknown)	5,033
Career 2022 or earlier, title not advisor, board member, investor or member	5,346
Career 2022 or earlier, 500+ connections	4,884
Career 2022 or earlier, left the Big Tech employer in 2026	609

The lesson from the table is that the raw definition is already clean: dropping students and first-jobbers removes 3%, not 30%. I pulled the raw definition and did the cuts on the rows.

The market brief

mode: "market", 168 aggregate rows, no person rows:

• Country: US 84%, UK 9%, Canada 5%, Germany 2%, Netherlands 1%.
• Current employer size (shares count current positions, so a founder with a board seat counts twice): 1–10 people 61%, unknown 44%, 11–50 18%. The big-company buckets are C-levels who left for a large company and people who hold a founder title alongside a job.
• Industry of the current company: unknown 42% (new companies have no industry on file yet), Technology and Internet 19%, Software 19%, Higher Education 9%, IT services 7%, Consulting 6%, VC and PE 3%.
• Top titles: Founder 25%, Co-Founder 14%, Chief Executive Officer 5%, Founder & CEO 4%, Chief Technology Officer 2.5%, Chief Operating Officer 1.2%.
• Top "employers": Stealth Startup 202 positions, Stealth AI Startup 115, Stealth 65. Then AWS, YouTube, TikTok, Adobe, Capital One, OpenAI, LinkedIn, in the tens.
• Personal email on record: at most 44%.

Three "stealth" spellings at the top of the employer column is the whole thesis of the list in one line.

Pulling the full list: two runs

mode: "people", profileDetail: "row", the same filters. One practical detail: Apify caps a single run at the account's remaining monthly limit, so the list came in two runs, the second excluding the first:

Run 1, maxResults: 4500: 11 minutes 32 seconds, 4,500 rows.
Run 2, the same input with "excludeDatasets": ["<dataset id of run 1>"] and maxResults: 2500: 6 minutes 19 seconds, 1,491 rows, and a note row saying 149 people were already delivered or repeated and were skipped.

Total: 5,991 distinct people. The count said 6,140 because it includes the historical snapshots the Actor later recognised as repeats and did not deliver twice.

What the rows showed:

Seniority	founder 4,407 · C-level 1,584
C-level titles	CEO 310 · CTO 170 · COO 68 · Chief of Staff 38 · CPO 37 · CMO 23
Current role started	2025: 3,284 · 2026: 757 · earlier: 1,950 (long-running side companies, and people who held the founder title before leaving)
Current company size	1–10: 2,680 · unknown: 1,876 · 11–50: 468 · 10,001+: 333 · 1,001–10,000: 302
Company founded in 2025 or 2026 (from the company card)	886, plus 293 founded in 2024
Company has a funding record	581
"Stealth" in the company name or headline	366
"YC" or "Y Combinator" in the company name or headline	112
AI, machine learning or LLM in the company name or headline	1,406 (23%)
Email flag on the row	personal 2,548 · work 3,719 · either 3,987 (67%)
Where	San Francisco and the Bay Area 722 · Seattle 442 · New York 422 · London 180 · Austin 135
Career started 2015 or earlier	4,307 (72%)
Rows with a second current position	2,640

The cut that matters for most of the readers above is "started the current role in 2025 or later, at a company of ten people or fewer, or with no size on file": 2,824 people. That is the pre-seed universe for these ten alumni networks in five countries, as of this week.

The shortlist at the profile tier

To see careers, not just titles, I pulled 300 people from the tightest cut at the profile tier with compact: true, which returns the five latest positions with dates, education and top skills in about 2 KB per person instead of the full record:

{
  "mode": "people",
  "profileDetail": "profile",
  "compact": true,
  "countries": ["us"],
  "pastEmployerDomains": ["google.com", "meta.com", "amazon.com", "microsoft.com", "apple.com",
                          "salesforce.com", "oracle.com", "cisco.com", "ibm.com", "intel.com"],
  "leftPastEmployerAfterYear": 2025,
  "seniority": ["founder", "cxo"],
  "currentRoleStartedAfterYear": 2025,
  "employeeCountMax": 50,
  "includeUnknownEmployerSize": true,
  "excludeTitleKeywords": ["advisor", "board member", "investor", "member"],
  "maxResults": 300
}

Run log: 3 minutes 27 seconds, 300 people, 294 with the full profile record and 4 with only the row-level record on file.

From the positions: of the 223 Big Tech exits visible in the five-position window, 58 were from Amazon, 54 Microsoft, 31 Google, 28 Meta, 20 Apple, 14 Salesforce, 10 Oracle, 6 IBM, 2 Intel. Exits are spread across every month of 2025 with bumps in June–July and December, and continue into the first quarter of 2026. Median tenure at the Big Tech position was two years, so these are mostly people who did a stint, not a career. Exactly 2 of the 300 Big Tech positions were internships. Headlines included two YC W26 companies, several "building in stealth", and a long tail of CPAs, consultants and advisors who use the founder title for a practice. Education was on file for 276 of 300.

The mistake: a work-email finder is the wrong key for founders

My reflex from the sales lists was to pass the shortlist to the email Actor. I sent 60 profile URLs to LinkedIn Email Finder: 57 came back with an address, and 54 of those were at a previous employer, often the Big Tech one the person had just left. Only 3 were at the current company. Of course: a two-month-old stealth startup has no email format on record.

For founders the right key is the personal mailbox, and the search has a tier for it. Same shortlist filter, profileDetail: "contacts", mustHave: ["personalEmail"]:

Run log: 48 seconds, 150 people delivered, 72 skipped for having no personal email. Of the 150: personal email 149, work email 139 (8 at the current company), direct phone 67, social links 92 (24 GitHub, 77 X).

That is the founder shortlist a seed fund or a startup bank would want: name, what they left and when, what they are building, a personal email, often a phone, and for a quarter of them a GitHub profile.

What to do with it, by use case

• Seed and angel investors: weekly run with excludeDatasets on last week's dataset, filter rows to company size 1–10 or unknown and role started this year, and the new founders arrive as a short list every Monday.
• Startup banks, cards, payroll, incorporation and accounting: the same rows filtered on the company card's founded year of 2025 or 2026. 886 companies this week.
• Cloud and developer-tool startup programs: add titleKeywords: ["cto", "technology", "engineering"] for the technical co-founders, and use the contacts tier for the GitHub links.
• Recruiters and job seekers: the 2,824 people at companies of ten or fewer who started in 2025 or later are hiring founding engineers before any job ad. Write to the founder, mention what they left, and ask what they are building.

More ways to use the alumni signal

Everything above is one combination of pastEmployerDomains, leftPastEmployerAfterYear and seniority. Other combinations I would run next, each a count away:

• Other alumni networks. Swap the ten domains for Stripe, Shopify, Uber and Airbnb, for the consulting firms, or for a biotech cluster. Every well-known company has a founder diaspora; most of them are not tracked by anyone.
• Second-time founders. Add pastTitleKeywords: ["founder", "ceo"]: people who have built before and left Big Tech again. A seed fund's favourite profile.
• Technical co-founders only. titleKeywords: ["cto", "technology", "engineering"], then the contacts tier for the GitHub links. For dev-tools startup programs and cloud credits.
• Angels inside the alumni. headlineKeywords: ["angel investor", "investor"] on the same 115,137 leavers: people with liquidity and a network, for fund LP outreach and syndicates.
• Big Tech hires at your accounts. Reverse the fields: companyDomains = your target accounts, pastEmployerDomains = the ten. New arrivals who expect Big Tech-grade tooling and will push for it.
• Between jobs. onlyBetweenJobs: true on the leavers: 30,840 people this week for recruiters, fractional-work platforms, courses and coaching products.
• Local founder lists. localityKeywords: ["london"] or ["berlin"] for accelerators, co-working spaces and city programs, refreshed weekly with excludeDatasets.
• Left this quarter. leftPastEmployerAfterYear: 2026 is 609 people with a career before 2022: the freshest version of the list, small enough to read by hand.

Agent prompt I used

In Claude Code with the Apify MCP server (setup in the series opener):

"Use b2bsearch/people-database-search. Past employers google.com, meta.com, amazon.com, microsoft.com, apple.com, salesforce.com, oracle.com, cisco.com, ibm.com, intel.com; left in 2025 or later; seniority founder or cxo; countries us, gb, de, nl, ca. Run mode count and mode market. Then one segments call comparing cuts by career start year, current role start, company size and excluded titles. Summarise and tell me what the top employers in the market view say about the segment. Do not run mode people without my confirmation; when I confirm, respect the maxResults I give and report rows delivered and rows skipped."

The agent noticed the "stealth" employers and the 44% unknown industry on its own and explained both correctly: new companies with no record yet. Then it waited.

For AI agents reading this

Actor b2bsearch/people-database-search. Alumni signal: pastEmployerDomains (site domains; brand domains resolve to the employer record) with leftPastEmployerAfterYear; the Actor excludes people still at those domains. Founders and executives: seniority: ["founder", "cxo"]. Tighten with currentRoleStartedAfterYear, employeeCountMax plus includeUnknownEmployerSize: true (new companies have no headcount on file), careerStartYearMax to drop interns and first-jobbers, excludeTitleKeywords for advisor and board titles. A single run is capped at the account's remaining monthly limit; split large pulls and pass earlier dataset ids in excludeDatasets. For founders use profileDetail: "contacts" with mustHave: ["personalEmail"] rather than a work-email finder; new companies have no work email format on record. Company card fields on each row: domain, employeeCount, industry, founded, funding. Per-tier pricing is on the Actor page; run mode: "count" and report it before any people run.

Next in the series: every sales leader who changed jobs in the last twelve months, in one query, refreshed weekly.