Use Case

Investor matching that learns and improves.

AI agents that score, match, and generate IC-ready reports — with compound intelligence that improves every run via Knowledge Graph.

01The Problem

Slow

Manual research across 10+ sources per investor. Days per shortlist.

Subjective

Scoring depends on who reviewed, not data. Inconsistent across rounds.

Doesn't learn

Start from zero every round. No memory of what worked last time.

02How It Works
01

Enrich investor profiles

Pull data from LinkedIn, Specter, and the web to build complete investor profiles — portfolio history, check sizes, sector focus, and activity signals.

02

AI scoring with configurable criteria

Score every investor against your custom criteria: thesis alignment, check size fit, portfolio overlap, geographic match, and stage preference.

03

Generate IC-ready reports

Automatically compile scored shortlists into Investment Committee reports with match rationale, risk flags, and recommended outreach angles.

04

Track prediction vs outcome

Knowledge Graph stores every match and its outcome. Scores recalibrate each run — the system gets smarter the more you use it.

03Results

3,000+

profiles scored

89%

prediction accuracy after 12 runs

Minutes

to IC-ready reports

04Integrations Used
LinkedInAffinitySpecterMistralKnowledge GraphEmailWeb Search

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