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The Science Behind the Platform

Your Network Is
Your Fundraise

We didn't build another CRM. We built on decades of network science to turn relationship chaos into your competitive edge.

↓ Explore the science
83%of successful referrals traverse weak tiesGranovetter, 1973
4.83average hops between any two VCsWatts & Strogatz, 1998
lower failure rate with top-networked VCsHochberg et al., 2007

Three Metaphors. Every Design Decision.

Core Loop

The Quarterback

See your pipeline, decide what matters, act in 1–2 clicks. The app doesn't play the game for you — it gives you field vision.

Architecture

The CastleFort

Your data stays within walls. Knowledge compounds behind the moat. Security and multi-tenant isolation are architectural, not afterthoughts.

Continuity

Institutional Memory

Fundraising is episodic — relationships are continuous. Round N+1 starts with the full knowledge of rounds 1 through N.

“The warm introduction you need is closer than you think.”
Built on 9 peer-reviewed papers across network science, VC research, and AI

Network Science

The Research That Drives Our Design

Our approach is grounded in decades of peer-reviewed research. Here are the four ideas that matter most.

83%

The Strength of Weak Ties

Your acquaintances — not your close friends — open access to entirely new investor clusters. We map your entire team's network, not just the close relationships.

Granovetter (1973), Burt (1992)
~5 hops

Small-World Navigation

Any VC is reachable in 2–4 introductions through shortcut edges. Our pathfinding models the distance-reducing potential of each hop, not just abstract shortest paths.

Watts & Strogatz (1998), Kleinberg (2000)
67%

Network Position Predicts Success

An investor's network position accounts for 67% of predictive power in outcome models. Companies backed by top-networked VCs have 2× lower failure rates.

Hochberg (2007), Bubna (2020)
3 layers

Three Networks, One Picture

Co-investment, affiliation, and social networks are distinct layers with independent signals. Most tools only capture one. We model all three.

Shi (2025), Poole (2025)

Want the full research? Dive deeper.

Living Intelligence

Data That Breathes

We don't think of data as static rows. We think of it as a living network — one that learns, strengthens, and sometimes fades, just like biological systems.

Neurons That Fire Together

Relationships follow Hebbian learning. When two entities co-appear in news, co-invest, or are mentioned together, their connection strengthens. Active relationships naturally rise to prominence.

Intelligent Decay

Unused connections decay — but not by simple time. A connection that was exercised weekly and goes silent for a month fades faster than one that was always quarterly. Decay is relative to expected activity.

The Cosmic Web

Co-investor cliques form galaxy groups. Investor communities coalesce into galaxy clusters. Bridge nodes and weak ties connect superclusters. And between the filaments of activity — voids, where new bridges create the most value.

How We Build

Six Design Principles

Architectural choices that shape what the platform can do and how much you can trust it.

01

Canonical + Overlay

Shared knowledge commons grows with every user. Your private annotations stay yours.

02

Epistemic Humility

Every data point carries a confidence score with explained provenance. The UI never lets you mistake speculation for fact.

03

Commons-Based Intelligence

One user's research benefits everyone. Individual contributions are anonymous; collective knowledge is shared.

04

Multi-Signal Scoring

No single source trusted alone. Confidence increases with source diversity, not volume.

05

Absence Taxonomy

Missing data isn't a black box. Every gap is classified: undiscovered, private, nonexistent, or expired.

06

Entity Resolution

The same person across CRM, news, and SEC filings — resolved with quantified confidence.

See the Science in Action

Every theory on this page maps to a real feature. Jump in.

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