---
title: Outcome Feedback Loop and Model Governance for Investor Matching
date: 2026-02-10
status: proposal
author: Larry the Laptop Lobster
---

# Outcome Feedback Loop and Model Governance for Investor Matching

**Author:** PlanExe Team  
**Date:** 2026-02-10  
**Status:** Proposal  
**Tags:** `feedback-loop`, `governance`, `mlops`, `evaluation`, `roi`

---

## Pitch

Close the loop between predicted and realized investment outcomes so the matching system continuously improves ROI accuracy, fairness, and trustworthiness.

## TL;DR

- Track each recommendation from match to long-term outcome.

- Compare predicted ROI/risk to realized performance.

- Retrain models with strict governance, versioning, and rollback.

- Publish model health dashboards for investors and operators.

## Problem

Without outcome feedback, matching systems drift and confidence erodes:

- Predictions can become stale as markets change.

- Biases persist unnoticed.

- Users cannot audit whether model recommendations are actually improving returns.

## Proposed Solution

Implement an **Outcome Intelligence Layer** that:

1. Captures lifecycle events (funded, milestones hit/missed, follow-on rounds, exits, write-downs)

2. Measures calibration and error by cohort, sector, and stage

3. Triggers retraining when quality degrades

4. Enforces governance gates before new model deployment

## Architecture

```text
┌──────────────────────────────┐
│ Matching & Recommendation    │
│ - Plan↔Investor rankings     │
│ - Predicted ROI + risk       │
└──────────────┬───────────────┘
               │ emits events
               ▼
┌──────────────────────────────┐
│ Outcome Event Store          │
│ - Funding events             │
│ - Milestone outcomes         │
│ - Valuation updates          │
└──────────────┬───────────────┘
               │
               ▼
┌──────────────────────────────┐
│ Evaluation & Drift Monitor   │
│ - Calibration                │
│ - Bias / fairness checks     │
│ - Segment error analysis     │
└──────────────┬───────────────┘
               │
               ▼
┌──────────────────────────────┐
│ MLOps Governance Pipeline    │
│ - Candidate model testing    │
│ - Human approval gates       │
│ - Versioned rollout/rollback │
└──────────────────────────────┘
```

## Implementation

### Phase 1: Outcome Telemetry

- Add immutable event log keyed by recommendation ID.

- Define canonical outcome windows (3/6/12/24/36 months).

- Attach confidence bands at recommendation time for later calibration checks.

### Phase 2: Evaluation Framework

- Track metrics by cohort:

  - calibration error, rank correlation with realized returns, false-positive funding recommendations.

- Detect drift in market regime and feature distributions.

- Run shadow-mode candidate models continuously.

### Phase 3: Governance + Transparency

- Require deployment gates:

  - minimum calibration improvement, no fairness regression, reproducible training artifact.

- Publish model cards and changelogs.

- Support one-click rollback to previous stable model.

## Success Metrics

- **Calibration Error:** -25% within 2 quarters.

- **Ranking Quality:** Higher Spearman correlation between predicted and realized ROI.

- **Fairness Stability:** No significant degradation across geography/sector/founder-background slices.

- **Trust Metric:** Increased investor acceptance of top recommendations.

## Risks

- **Long feedback cycles in venture outcomes** → Use intermediate leading indicators and survival analysis.

- **Attribution ambiguity** → Separate model recommendation quality from post-investment support effects.

- **Privacy and compliance** → Differential access control and auditable data lineage.

- **Operational overhead** → Automate evaluation and gating workflows.

## Why This Matters

A matching engine is only valuable if it stays correct over time. Governance plus feedback transforms it from a static ranking tool into a reliable capital allocation system that compounds ROI advantage.

## Detailed Implementation Plan

### Phase A — Outcome Telemetry Contract

1. Define standardized outcome schema (win/loss, cost variance, schedule variance, quality outcome).
2. Build ingestion endpoints and integrity checks.
3. Add outcome confidence and source provenance fields.

### Phase B — Governance Evaluation Loop

1. Compare model predictions vs realized outcomes.
2. Flag drift and underperforming model components.
3. Trigger review workflows for retraining or policy changes.

### Phase C — Governance Board Outputs

1. Generate periodic model health reports.
2. Maintain change logs for model updates and rationale.
3. Require signoff on high-impact model policy changes.

### Validation Checklist

- Outcome ingestion completeness
- Drift detection sensitivity/specificity
- Governance decision turnaround SLAs

