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Investor Thesis Matching Engine

Author: PlanExe Team
Date: 2026-02-10
Status: Proposal
Tags: investors, matching, roi, ranking, marketplace

Pitch

Build a Kickstarter-like discovery and funding layer where projects are matched to investors by expected risk-adjusted ROI and explicit thesis fit, not by founder charisma or social reach.

TL;DR

  • Convert every plan into a normalized feature vector (market, margin, burn, moat, timeline, execution risk).

  • Convert every investor into a thesis vector (stage, sector, check size, target return, risk appetite, hold period).

  • Score plan↔investor fit using explainable ranking.

  • Show both sides a transparent “why this match” report.

  • Goal: improve conversion rate, reduce time-to-first-commitment, and increase realized IRR.

Problem

Current startup discovery is noisy and personality-driven:

  • Strong projects can be underfunded if founders are weak at storytelling.

  • Investors spend too much time filtering poor-fit deals.

  • Match quality is opaque; post-hoc outcome learning is weak.

Proposed Solution

Introduce a deterministic, data-first matching service that ranks investor-project pairs using:

  1. Thesis compatibility (hard constraints + soft preferences)

  2. Projected ROI (expected value with uncertainty)

  3. Execution confidence (evidence-weighted feasibility)

  4. Diversification impact (marginal portfolio contribution)

Hypotheses To Validate

We should explicitly test three core hypotheses before scaling. A and B are foundational; C expands the engine beyond conventional startup finance and tests whether the core thesis-matching approach generalizes to large, complex, and often public-interest projects.

A. Thesis-Fit Improves Deal Quality

Claim: A structured thesis profile plus plan feature vector improves match quality versus status-quo discovery.

What to confirm:

  • Investors engage more with top-ranked opportunities (Precision@10 and click-to-diligence rate increase).
  • Founders receive higher-quality intros (higher reply rate and faster scheduling).
  • The “why-match” explanation increases investor trust and reduces time-to-no.

B. Risk-Adjusted ROI Scoring Drives Better Outcomes

Claim: Incorporating scenario-based ROI and execution confidence leads to better post-investment performance than thesis-fit alone.

What to confirm:

  • Matched deals show higher realized IRR or MOIC in historical backtests.
  • Rankings remain stable under reasonable perturbations of assumptions.
  • Investors accept the model’s uncertainty intervals as decision-relevant.

C. Cross-Sector Generalization Is Feasible

Claim: The matching engine can be extended beyond VC-style deals to infrastructure, public-interest, and climate projects with different financing structures.

What to confirm:

  • The same vector-based thesis/plan representation can be adapted with domain-specific features.
  • The scoring logic can handle non-VC return models (availability payments, blended finance, concession revenues).
  • Stakeholder fit and risk allocation can be represented as constraints and preferences.

Hypothesis Examples At Different Scales

Below are three example project archetypes and the specific hypothesis checks they would drive. These are not full plans, just test cases for validating A/B/C in different settings.

1) Expensive Huge Bridge Project Between Two Countries

Example thesis match:

  • Infrastructure funds targeting long-duration, low-volatility returns.
  • Sovereign wealth funds focused on strategic trade corridors.
  • Development banks with regional connectivity mandates.

Key hypothesis checks:

  • A: Do investors who prioritize long-term, inflation-linked cashflows engage more with the bridge than generalists?
  • B: Does scenario modeling (traffic volumes, tariff policy, FX risk) meaningfully change the ranking?
  • C: Can concession structure, political risk, and cross-border governance be represented as structured features and constraints?

2) Famine Prevention In A Poor Country

Example thesis match:

  • Impact funds targeting humanitarian outcomes with blended finance.
  • Philanthropic capital with strict outcome metrics (lives saved, malnutrition reduction).
  • Multilateral agencies with food security mandates.

Key hypothesis checks:

  • A: Does explicit outcome alignment (e.g., DALYs reduced, resilience score) improve match quality?
  • B: Can risk-adjusted ROI be replaced or augmented with cost-effectiveness or outcome ROI?
  • C: Can non-financial return frameworks be integrated without breaking the ranking model?

3) Deforestation Prevention In Brazil

Example thesis match:

  • Climate funds and corporates seeking verified carbon credits.
  • ESG-focused investors with biodiversity preservation targets.
  • Government-backed programs with enforcement support.

Key hypothesis checks:

  • A: Do investors with explicit climate/ESG theses show higher engagement than generic funds?
  • B: Does the model correctly weigh uncertainties (regulatory enforcement, land rights, carbon price volatility)?
  • C: Can verification and permanence risk be encoded as features that materially affect match ranking?

Architecture

┌────────────────────────────┐
│ Plan Ingestion             │
│ - PlanExe structured plan  │
│ - Financial assumptions    │
│ - Milestones + risks       │
└─────────────┬──────────────┘
              │
              ▼
┌────────────────────────────┐
│ Feature Engineering        │
│ - Unit economics           │
│ - Market indicators        │
│ - Risk factors             │
└─────────────┬──────────────┘
              │
              ▼
┌────────────────────────────┐      ┌──────────────────────────┐
│ Matching & Scoring API     │◄────►│ Investor Thesis Profiles │
│ - Constraint filtering     │      │ - Return targets         │
│ - Fit + ROI ranking        │      │ - Risk + sector rules    │
│ - Explainability layer     │      │ - Check size constraints │
└─────────────┬──────────────┘      └──────────────────────────┘
              │
              ▼
┌────────────────────────────┐
│ Marketplace UI             │
│ - Ranked opportunities     │
│ - Why-match report         │
│ - Confidence intervals     │
└────────────────────────────┘

Implementation

Phase 1: Data Model + Constraint Engine

  • Extend plan schema with investor-relevant fields:

  • TAM/SAM/SOM, CAC, LTV, gross margin, payback period, capital required, runway, regulatory risk.

  • Add investor profile schema:

  • sectors, geography, stage, check range, target MOIC/IRR, max drawdown tolerance.

  • Implement hard-filter pass (exclude impossible matches first).

Phase 2: ROI + Fit Scoring

  • Create weighted scoring function:

  • FinalScore = 0.45*ThesisFit + 0.35*RiskAdjustedROI + 0.20*ExecutionConfidence

  • Compute uncertainty-aware ROI using scenario bands (bear/base/bull).

  • Add explainability payload per recommendation (top positive and negative drivers).

Phase 3: Marketplace Integration

  • Investor dashboard: ranked list + confidence intervals + sensitivity to assumptions.

  • Founder dashboard: “best-fit investors” ordered by thesis overlap and probability of commitment.

  • Feedback capture on passes/commits to retrain weights.

Success Metrics

  • Match Precision@10: ≥ 0.65 (investor engages with 6.5/10 top-ranked opportunities)

  • Time-to-First-Term-Sheet: -30% vs baseline

  • Qualified Intro Conversion: +40%

  • Post-Investment IRR Lift: +10% at cohort level

  • Cold-start Coverage: ≥ 90% of new plans receive at least 5 viable investor matches

Risks

  • Biased historical outcomes → Use counterfactual evaluation and fairness constraints.

  • Overfitting to short-term wins → Optimize for multi-horizon outcomes (12/24/36 months).

  • Gaming by founders → Add evidence verification and anomaly detection.

  • Investor strategy drift → Prompt quarterly thesis re-validation.

Why This Matters

This proposal shifts fundraising from persuasion-first to evidence-first. It helps credible, high-upside plans get surfaced even when founders are not exceptional marketers, improving capital allocation efficiency for everyone.

Detailed Implementation Plan

Phase A — Thesis Schema and Intake

  1. Define investor thesis schema (sector, ticket size, geography, stage, constraints).
  2. Ingest and normalize investor profile records.
  3. Add confidence labels for inferred thesis signals.

Phase B — Matching Engine

  1. Compute thesis-plan alignment with weighted feature scoring.
  2. Add exclusion filters (hard constraints).
  3. Produce explainable match reasons and mismatch flags.

Phase C — Feedback Loop

  1. Capture investor response outcomes.
  2. Tune matching weights with outcome data.
  3. Add cold-start defaults by investor archetype.

Validation Checklist

  • Precision of top matches
  • Response-rate uplift vs baseline outreach
  • Explainability quality review