Startup Investor Database
A curated database of 10,469 venture capital, angel, accelerator, private equity and family-office firms, filterable by firm type, industry focus, investment stage and country — with verified partner and principal contacts available per firm.
RECORDS10,469
MEDIAN LAGrefreshed periodically
PARAMETERS10
TOTAL USERS548
MONTHLY ACTIVE37
RUNS SUCCEEDED94.9%
RATING2.29 (11)
Input parameters
| PARAMETER | TYPE | REQ | DEFAULT | DESCRIPTION |
|---|---|---|---|---|
| Firm_Types | string[] | no | — | 17 types: Venture Capital, Angel, Accelerator, Incubator, Private Equity, Family Office, Hedge Fund, Venture Debt +9 |
| Focus_Areas | string[] | no | — | 49 industries: AI, Biotechnology, Fintech, Health Care, Climate, Gaming +43 |
| Investment_Stages | string[] | no | — | Pre-Seed, Seed, Series A–D, Late Stage, Growth, Mezzanine, IPO, Debt |
| Countries | string[] | no | — | Full English country names of the firm's headquarters |
| Keyword | string | no | — | Free text across firm names, descriptions and thesis language |
| Max_Results | integer | no | 100 | 1–10000. Every firm is billed, so narrow with a filter before raising it. — drives your bill |
| Offset | integer | no | 0 | Skip N firms; pair with Max_Results to paginate across runs |
| Order_By | enum | no | created_at | created_at · firm_name · firm_country · firm_type_id |
| Order_Direction | enum | no | desc | asc or desc |
| Include_Contacts | boolean | no | false | Attach the investor_contacts array; billed per contact — drives your bill |
Worked examples
{ "Keyword": "climate" }
{
"Firm_Types": ["Venture Capital Investor"],
"Focus_Areas": ["Artificial Intelligence"],
"Investment_Stages": ["Pre-Seed", "Seed"]
}
{
"Firm_Types": ["Angel Investor"],
"Countries": ["United Kingdom"],
"Include_Contacts": true,
"Max_Results": 50
}
{
"Focus_Areas": ["Health Care"],
"Max_Results": 1000,
"Offset": 1000,
"Order_By": "firm_name"
}
POWER-USER TIP
Sort on firm_name before paginating — The default created_at ordering is not unique, so rows can shift between runs and a paged export ends up with gaps or duplicates. Set Order_By to firm_name and the pagination is stable across runs.
POWER-USER TIP
Filter before raising Max_Results — Every firm returned is billed. Max_Results at 10000 with no filters is a full-database pull and prices accordingly — narrow with Keyword or Firm_Types first, then widen.
Coverage
10,469
investor firms — venture, angel, accelerator, PE, family office
17
firm types — VC · Angel · Accelerator · Incubator · PE · Family Office +11
49
focus areas — AI · Biotech · Fintech · Health Care · Climate +44
Tasks
Saved runs with the inputs already filled in — each one a standalone page and a working configuration example.
Code
curl
curl -X POST "https://api.apify.com/v2/acts/johnvc~startup-investors-data-scraper/run-sync-get-dataset-items" \
-H "Authorization: Bearer $APIFY_TOKEN" \
-H "Content-Type: application/json" \
-d '{"Focus_Areas":["Artificial Intelligence"],"Investment_Stages":["Seed"],"Max_Results":100}'
Python
from apify_client import ApifyClient
client = ApifyClient("APIFY_TOKEN")
run = client.actor("johnvc/startup-investors-data-scraper").call(
run_input={"Keyword": "climate", "Include_Contacts": True, "Max_Results": 50}
)
for firm in client.dataset(run.default_dataset_id).iterate_items():
print(firm.get("firm_name"), len(firm.get("investor_contacts") or []))
What people use it for
- Building a targeted fundraising list
- Mapping which firms back a given sector
- Sourcing warm intros through partner contacts
- Competitive landscape research before a raise
- Selling into venture firms as customers
- Academic study of capital allocation