---
title: Expert Discovery + Fit Scoring for Plan Verification
date: 2026-02-10
status: proposal
author: Larry the Laptop Lobster
---

# Expert Discovery + Fit Scoring for Plan Verification

## Pitch
Automatically identify and rank qualified experts for plan verification using a structured fit scoring model that balances domain expertise, availability, cost, and reputation.

## Why
Verification requires the right experts, but manual discovery is slow and unreliable. Fit scoring streamlines selection while maintaining quality and accountability.

## Problem

- Expert discovery is ad hoc and time-consuming.
- Expertise is not normalized across domains.
- Cost and availability trade-offs are poorly quantified.

## Proposed Solution
Build a system that:

1. Extracts verification requirements from a plan.
2. Queries an expert registry and external sources.
3. Scores experts by fit and ranks the best matches.
4. Produces an explainable recommendation list.

## Fit Scoring Model

### Inputs

- Domain match (primary and secondary expertise)
- Verification experience and prior outcomes
- Availability and turnaround time
- Cost relative to budget constraints
- Reputation score from marketplace

### Example Formula

```
FitScore =
  0.35*DomainMatch +
  0.25*Reputation +
  0.20*Availability +
  0.10*CostFit +
  0.10*OutcomeHistory
```

## Expert Registry Schema

```json
{
  "expert_id": "exp_441",
  "domains": ["energy", "regulation"],
  "credentials": ["PE", "PhD"],
  "availability_days": 7,
  "hourly_rate": 180,
  "reputation_score": 0.86
}
```

## Output Schema

```json
{
  "plan_id": "plan_007",
  "ranked_experts": [
    {"expert_id": "exp_441", "fit_score": 0.89, "reason": "Strong domain match"},
    {"expert_id": "exp_208", "fit_score": 0.81, "reason": "Fast turnaround"}
  ]
}
```

## Matching Workflow

### 1) Requirement Extraction

- Identify required domains, claim types, and regulatory context.
- Tag the plan with complexity and risk tiers.

### 2) Candidate Retrieval

- Query registry by domain and geography.
- Filter by minimum credentials and availability.
- Exclude conflicts of interest.

### 3) Fit Scoring

- Compute fit score and provide reason codes.
- Allow human override when the plan is high-stakes.

### 4) Assignment

- Auto-assign top experts or present ranked list to reviewer.
- Track acceptance and response latency.

## Integration Points

- Feeds into multi-stage verification workflow.
- Uses reputation scores from expert marketplace.
- Supports governance and conflict-of-interest checks.

## Success Metrics

- Reduced time to match experts.
- Higher verification completion rates.
- Improved investor confidence in verification process.

## Risks

- Incomplete expert data: mitigate with periodic profile verification.
- Cost bias against high-quality experts: allow weighted trade-offs.
- Bias in reputation scoring: normalize by domain and sample size.

## Future Enhancements

- External credential validation integration.
- Automated discovery from publications and patents.
- Adaptive scoring by project complexity.

## Detailed Implementation Plan

### Phase A — Expert Ontology + Data Connectors (2–3 weeks)

1. Define a normalized expert ontology:
   - domains/subdomains
   - credential classes
   - region/jurisdiction tags
   - project-type experience tags
2. Build ingestion connectors for curated sources (registries, publications, procurement records, verified profiles).
3. Add entity resolution to deduplicate the same expert across sources.

### Phase B — Fit Scoring Service (2 weeks)

1. Implement feature extraction:
   - domain similarity to plan
   - project similarity to past verified work
   - jurisdiction compatibility
   - credential strength and recency
   - availability signals
2. Implement weighted scoring with configurable policy weights per domain.
3. Emit ranked shortlist with per-expert rationale and confidence.

### Phase C — User Workflow + Outreach (2 weeks)

1. Add expert shortlist panel in plan UI.
2. Generate outreach packets from plan context (scope, constraints, expected review role).
3. Track responses and status transitions (invited, accepted, declined, completed).

### Data model additions

- `experts`
- `expert_profiles`
- `expert_source_records`
- `expert_fit_scores`
- `expert_outreach_events`

### Validation checklist

- Dedup precision on merged profiles
- Ranking quality judged by human reviewers
- Time-to-first-accepted-expert
- Coverage of required review domains per plan

