Harambe Ventures

This section is a point-in-time snapshot of this fund’s portfolio as it exists in our database: how many companies fall into each health state (alive, exited, zombie, dead, unknown), what has moved recently, and a small set of high-level signals.

Use it the same way you would use a portfolio factsheet in a data room: orientation before you go deeper. The score breakdown, when present, shows how the headline score is composed. It is not a valuation, a mark, or a forecast of fund performance.

This is not a complete audit. Coverage depends on ingestion completeness. Treat gaps and “unknown” labels as an invitation to verify off-platform.

If a company appears more than once, that usually reflects multiple investment edges (rounds) in the data model.

A compact view of what the fund says it invests in: the geographies it prioritises, the stages it backs, the primary sectors it focuses on, and its typical ticket size. Data comes from the fund's managed profile and observed portfolio activity.

Use it as a quick orientation layer before reading portfolio data: it tells you what the fund thinks it is, which you can cross-check against what it has actually done in sections 4–17.

This is not a legal investment mandate and not a binding commitment. It reflects self-stated focus and observed patterns, which may diverge.

If regions or sectors look off, the fund owner can update their managed profile or trigger a context rebuild to refresh this section.

A discovery list of companies that look like a reasonable thematic, stage, or regional fit given what we know about the fund and the company.

Treat this as a sourcing shortlist to research: a way to see where the model thinks adjacent dealflow might sit, not a recommendation to buy, sell, or pass.

It is not investment advice, a fairness opinion, or a statement that any company is raising or investable.

If two companies look similar, compare round context and ownership in your own systems.

Other investors that “look like” this fund under a few stable dimensions: geography, stage, and tag overlap.

Use it to place the fund in a peer cluster and understand the syndicate neighborhood you might see in a cap table.

It is not a performance benchmark. Similarity does not imply similar returns or portfolio construction.

“Twins” can include funds with different stated strategies if their realized portfolios overlap.

Portfolio startup twins spotlight individual holdings and surface market companies that resemble them on taxonomy, stage, and signals.

Use it as pattern recognition across your book: for each highlighted name, read why the twins look alike and how those comparables are evolving.

This is not a pipeline of deals to execute, nor verification that any twin is raising.

A small candidate pool means fewer close matches exist in our data, so read those twins more loosely.

A thematic and momentum view built on tags that appear in this portfolio: activity, funding, year-over-year change, and how crowded a tag is.

Use it to see concentration risk and narrative heat.

It is not a GICS-style sector taxonomy, and not a full market-sizing exercise.

High crowding means a lot of capital and attention already hunting in the same label bucket.

A list of repeat founders whose profile is close enough to the fund’s focus to be worth a sourcing and network map.

Use it as a diligence and conversation backplane: which repeat builders overlap with the fund’s world.

It is not an endorsement, and not a list of “best founders.”

Read why a founder is listed: overlap with categories or regions can be more informative than a raw relevance number.

Themes or tag buckets where, relative to a simple benchmark, there may be strategic room.

Use it as a strategic question generator: “If we run this strategy, should we be here?”

It is not a list of guaranteed opportunities; a low score does not mean a theme is bad.

Cross-check with the fund’s own thesis and portfolio construction rules.

A rough outcome mix by sector compared with regional and global baselines from the same data universe.

Use it to see whether the fund’s book, in a given bucket, leans healthier or harsher than what we typically observe elsewhere.

It is not a time-to-exit model, not a mark-to-mark, and not a full hazard rate analysis.

Differences in the definition of “zombie” or “exited” between systems can move rates by several points.

A comparative cut of how other investors’ books look in categories that matter to this fund.

Use it to calibrate whether you are a large player, a small player, or a specialist in a theme.

Peer definitions are always imperfect; the peer set is a foggy mirror, not a perfect peer group.

Divergence between peer trends and survival tables usually comes from different buckets or time windows.

A tag roll-up of risk-style signals across portfolio companies: a concentration and pattern view.

Use it to structure questions in diligence about where stress would show up first.

It is not a statement that any company will fail, and not legal or compliance advice.

A high risk score in a tag often means clustering, not a single “bad” company.

A framing of reserve deployment: companies plausibly at a next financing stage, with historical conversion patterns by category.

Use it in conversations about reserves, pacing, and pro-rata discipline.

It is not a prediction of who will raise, at what price, or with whom leading.

If data is sparse in a category, conversion should be read qualitatively.

A view of exit-like outcomes and exit rate patterns in categories relevant to the portfolio.

Use it to set expectations on how often exits appear in a theme or geography in our data.

It is not a list of the fund’s actual realized exits unless those companies are in our set with updated status.

Compare count to time held in the real portfolio outside this system.

A relative comparison of deal size and stage mix by geography in our data.

Use it to reality-check expansion stories.

It is not macroeconomic advice, FX forecasting, or a map of where to move next.

A country with a higher median check is not “better” for every strategy.

A map of the syndicate environment: which other investors show up in cap tables with this fund.

Use it to test stories about access and reputation.

It is not a “good investor / bad investor” list.

If you have policy constraints on co-investors, use this as a first pass for pattern, then verify in cap tables.

When the export includes one, this is a structured narrative memo grouped into the same areas as sections 2–15.

After scanning the headline tables, or before meetings if you want paragraph context.

Not legal, tax or investment advice; not authoritative when it conflicts with the tables.

Opening bold framing line (if present) summarises tension only: reconcile every claim with sections 2–15.