Find investor fit

The goal

Replace a long investor list with a research queue

Most fundraising searches begin with investor labels and geography. Portfolio evidence is stronger: it shows who has already backed companies in a related market.

The result is not an automated recommendation. It is a ranked starting point for checking thesis, conflicts, fund status, partner ownership, and warm introductions.

python -m venv .venv
source .venv/bin/activate
pip install requests python-dotenv

Store DEALROOM_CLIENT_ID and DEALROOM_CLIENT_SECRET in a local .env file. Keep that file out of version control.

Use portfolio history as evidence

Two query parameters add a portfolio match count to every investor record. When both are present, the count reflects investments matching the selected tags and portfolio location.

Keep the count visible. A user should be able to distinguish a firm with repeated market experience from one with a single adjacent investment.

Choose a few diagnostic tags

Cerrion has broad tags such as AI and Deep Tech, but those produce a noisy investor pool. This workflow uses three tags that describe its specific commercial context.

focus_tags = [99301, 2117101, 10016503]

query = {
    "portfolio_count_tag": "|".join(map(str, focus_tags)),
    "portfolio_count_location": "34",
}

Resolve tag and location IDs through the reference endpoints in a general application. The downloadable example also verifies that every selected tag is attached to the target company.

Remove investors that cannot fit the round

Portfolio relevance alone can surface inactive investors or firms whose normal check size misses the target. Add a filter before ranking.

investor_filter = (
    "and(last_investor_round_date[gte]:2024,"
    "min_deal_size[lte]:25000000,"
    "max_deal_size[gte]:5000000)"
)

The two deal-size conditions test range overlap. The investor's minimum must not exceed your ceiling, and its maximum must reach your floor.

Rank by evidence, then inspect context

Request the match count and preserve the server ordering. The response also carries investment stages, deal sizes, recent activity, investor type, and headline portfolio metrics.

response = client.get(
    "/data/investors",
    {
        **query,
        "filter": investor_filter,
        "sort": "-portfolio_match_count,investor_rank",
        "limit": 12,
        "include_total": "true",
        "currency": "USD",
    },
)

The script adds concise reasons from returned fields but never changes the API ranking. This keeps portfolio evidence separate from later human judgment.

Real output

Snapshot generated from the Dealroom API. Re-run the script for current investor activity and portfolio data.

Validate fit before contacting anyone

A high portfolio match count earns an investor a place in the research queue. It does not complete the fundraising work.

• Check whether a relevant portfolio company creates a competitive conflict.
• Confirm the investor is deploying from a current fund.
• Review target ownership, reserves, geography, and lead behavior.
• Identify the partner who owns the relevant thesis.
• Use recent deals and shared relationships to plan a credible introduction.