# AI Case-Intake Triage Reproducibility + Adverse-Impact Snapshot

**== SAMPLE / GENERIC EXAMPLE — SYNTHETIC DATA, NOT A CLIENT ENGAGEMENT ==**

**Format:** contrarianAI Independent-Verifier $499 Snapshot (v0.9 sensor)
**Snapshot generation date:** 2026-07-21
**Snapshot ID:** CAI-PF-GEN-DEMO-5e9c08934c26e1fff1fa3128

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## 1. Scope of Snapshot

This SAMPLE Snapshot addresses a synthetic mid-market plaintiff PI firm's AI-driven case-intake triage classifier. Scenario details:

- **Firm profile:** ~$500M-$2B recovered / 5,000-20,000 active matters annually / mid-market plaintiff PI firm
- **AI system in scope:** case-intake triage classifier (mock model version case-intake-triage-v2.4.7)
- **Audit period:** 2026-04-15 through 2026-07-13 (90 days)
- **Total intakes analyzed:** 4,969
- **Baseline window:** first 30 days (1,572 intakes)
- **Regulatory framework applied:**
  - Federal Rule of Civil Procedure 37(e) — spoliation duty on AI-driven decisions
  - State bar AI-competence rulings (35+ states as of March 2026; California COPRAC enforceable rule)
  - Malpractice-carrier questionnaire items (Aon / Marsh / Beazley / CNA legal-liability desk trend)
  - Adverse-impact statistical methodology (Rule 23 class-adjacent exposure)
  - FCC TCPA (if any AI-generated communications component)

---

## 2. Sensor summary

- **Sensor version:** contrarianAI-distsensor-v0.9
- **Sensor family:** distributional-shape (independent of production model family)
- **Independence:** sensor operates on the case-intake AI's output stream only. Distinct from the production model family. Different mathematical basis (distributional-shape statistics vs gradient-boosted classifier). Different retention pipeline. contrarianAI holds no equity, employment, or vendor relationship with any legal-AI vendor (Eve / Supio / Darrow / Harvey / etc.).

- **Detection thresholds active:**
  - Mean-shift threshold: 1.5 standard deviations from baseline
  - Variance-ratio threshold: 1.5x baseline
  - KL-divergence threshold: 0.2
  - Cohort adverse-impact differential threshold: 12% shift across cohorts

---

## 3. Findings summary

- **Total drift events detected:** 16
- **High severity:** 7
- **Medium severity:** 9
- **First drift day (post-baseline):** Day 30
- **Critical adverse-impact-differential signal flagged:** NO

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## 4. Overall determination

**CATEGORY C -- INSUFFICIENT FOR ATTESTATION**

Multiple high-severity distributional-drift events detected in the AI case-intake triage engine across the audit period. Firm should not represent the case-intake triage system as FRCP 37(e)-defensible or bar-ethics-compliant until named remediation completes and independent re-audit confirms closure.

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## 5. The 3 fix-first items scoped to this AI surface

1. **Freeze or hold-for-review** all post-drift AI-generated triage decisions in Cohort C3 (and other flagged cohorts) until adverse-impact-differential validation completes. Document the pause + resumption criteria in the compliance program.
2. **Retain evidence artifacts** for every intake in the audit period — model version pin, input snapshot, tool-call trace, config state at decision-time. FRCP 37(e) sanctions apply to any deleted-or-not-retained artifact once litigation is reasonably anticipated. Deletion holds should be issued now.
3. **Add adaptive distributional-monitoring** to production case-intake pipeline. Point-in-time attestation alone is insufficient; the between-audit blind spot is where FRCP 37 exposure silently accumulates. Real-time cohort-differential monitoring is the operational fix.

---

## 6. Detailed drift events

Each event includes date, sensor type, affected client cohort, severity, and plain-language detail describing what the sensor observed in the case-intake AI's decision stream.

### Day 30 (2026-05-15) -- cohort_distribution_divergence (cohort C1)
- **Severity:** medium
- **Detail:** Cohort C1 score distribution shape has diverged from baseline (KL=0.344). Bimodal-emergence or long-tail-shift likely.
- **KL-divergence:** 0.3443

### Day 30 (2026-05-15) -- cohort_distribution_divergence (cohort C2)
- **Severity:** medium
- **Detail:** Cohort C2 score distribution shape has diverged from baseline (KL=0.298). Bimodal-emergence or long-tail-shift likely.
- **KL-divergence:** 0.2981

### Day 30 (2026-05-15) -- cohort_distribution_divergence (cohort C4)
- **Severity:** medium
- **Detail:** Cohort C4 score distribution shape has diverged from baseline (KL=0.384). Bimodal-emergence or long-tail-shift likely.
- **KL-divergence:** 0.3837

### Day 37 (2026-05-22) -- cohort_distribution_divergence (cohort C5)
- **Severity:** high
- **Detail:** Cohort C5 score distribution shape has diverged from baseline (KL=0.707). Bimodal-emergence or long-tail-shift likely.
- **KL-divergence:** 0.7074

### Day 37 (2026-05-22) -- cohort_distribution_divergence (cohort C4)
- **Severity:** medium
- **Detail:** Cohort C4 score distribution shape has diverged from baseline (KL=0.318). Bimodal-emergence or long-tail-shift likely.
- **KL-divergence:** 0.3178

### Day 44 (2026-05-29) -- cohort_distribution_divergence (cohort C2)
- **Severity:** medium
- **Detail:** Cohort C2 score distribution shape has diverged from baseline (KL=0.340). Bimodal-emergence or long-tail-shift likely.
- **KL-divergence:** 0.3398

### Day 51 (2026-06-05) -- cohort_distribution_divergence (cohort C1)
- **Severity:** high
- **Detail:** Cohort C1 score distribution shape has diverged from baseline (KL=0.603). Bimodal-emergence or long-tail-shift likely.
- **KL-divergence:** 0.6034

### Day 51 (2026-06-05) -- cohort_distribution_divergence (cohort C4)
- **Severity:** high
- **Detail:** Cohort C4 score distribution shape has diverged from baseline (KL=0.600). Bimodal-emergence or long-tail-shift likely.
- **KL-divergence:** 0.5997

### Day 51 (2026-06-05) -- cohort_distribution_divergence (cohort C5)
- **Severity:** high
- **Detail:** Cohort C5 score distribution shape has diverged from baseline (KL=2.254). Bimodal-emergence or long-tail-shift likely.
- **KL-divergence:** 2.2537

### Day 51 (2026-06-05) -- cohort_distribution_divergence (cohort C2)
- **Severity:** medium
- **Detail:** Cohort C2 score distribution shape has diverged from baseline (KL=0.373). Bimodal-emergence or long-tail-shift likely.
- **KL-divergence:** 0.3729

### Day 58 (2026-06-12) -- cohort_distribution_divergence (cohort C2)
- **Severity:** high
- **Detail:** Cohort C2 score distribution shape has diverged from baseline (KL=0.402). Bimodal-emergence or long-tail-shift likely.
- **KL-divergence:** 0.4018

### Day 58 (2026-06-12) -- cohort_distribution_divergence (cohort C5)
- **Severity:** medium
- **Detail:** Cohort C5 score distribution shape has diverged from baseline (KL=0.306). Bimodal-emergence or long-tail-shift likely.
- **KL-divergence:** 0.3056

### Day 79 (2026-07-03) -- cohort_distribution_divergence (cohort C5)
- **Severity:** high
- **Detail:** Cohort C5 score distribution shape has diverged from baseline (KL=0.963). Bimodal-emergence or long-tail-shift likely.
- **KL-divergence:** 0.9634

### Day 79 (2026-07-03) -- cohort_distribution_divergence (cohort C2)
- **Severity:** medium
- **Detail:** Cohort C2 score distribution shape has diverged from baseline (KL=0.360). Bimodal-emergence or long-tail-shift likely.
- **KL-divergence:** 0.3595

### Day 86 (2026-07-10) -- cohort_distribution_divergence (cohort C4)
- **Severity:** high
- **Detail:** Cohort C4 score distribution shape has diverged from baseline (KL=0.804). Bimodal-emergence or long-tail-shift likely.
- **KL-divergence:** 0.8042

### Day 86 (2026-07-10) -- cohort_distribution_divergence (cohort C3)
- **Severity:** medium
- **Detail:** Cohort C3 score distribution shape has diverged from baseline (KL=0.398). Bimodal-emergence or long-tail-shift likely.
- **KL-divergence:** 0.3983

---

## 7. Counterparty-question rehearsal

**FRCP 37(e) discovery-motion question the firm's own defense counsel would demand:**

> "Reproduce this AI-generated case-intake triage decision from Day 47, including the client's stated case type + injury severity + estimated value as they existed at intake-time, the AI's inputs, its triage-score output, the lane routed, the model version, and the configuration state — as a defensible record we can hand to plaintiff-class counsel, defense counsel, or the presiding judge."

**Sample intake decisions from Day 47:**

| Intake ID | Triage Score | Lane | Model Version | Decision Hash |
|-----------|--------------|------|---------------|---------------|
| INT-002534 | 20.88 | Fast-Track Settle | case-intake-triage-v2.4.7 | `cip-002534` |
| INT-002533 | 60.28 | Standard Adjuster Review | case-intake-triage-v2.4.7 | `cip-002533` |
| INT-002532 | 80.52 | SIU / Fraud Review | case-intake-triage-v2.4.7 | `cip-002532` |
| INT-002531 | 59.34 | Standard Adjuster Review | case-intake-triage-v2.4.7 | `cip-002531` |
| INT-002525 | 57.33 | Standard Adjuster Review | case-intake-triage-v2.4.7 | `cip-002525` |

**Reproduction status:** all 5 sampled Day-47 intake decisions can be reproduced with defensible-records match: model version pinned, decision hash cryptographically bound, input data (client cohort + case type + injury severity + estimated value at intake-time) retained via decision-provenance record. Full inputs available via decision_hash lookup in the executable-action ledger (AIC layer #7) + time-of-decision knowledge snapshot (AIC layer #8).

**Retention:** artifacts retained for the malpractice-carrier tail horizon (7+ years for legal-liability) plus FRCP 37(e) litigation-hold discipline.

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## 8. What full Enterprise Attestation adds beyond this Snapshot

This $499 Snapshot addresses ONE AI system (case-intake triage classifier) at the SAMPLE firm. A full Enterprise Attestation ($35-55K / 3-6 weeks) covers:

- **All 8 AIC layers** (this Snapshot covers layers 1-4 + 6; Enterprise adds layers 5, 7, 8)
- **Full AI surface inventory** (case-intake + medical-records extraction + demand-drafting + contract-review + lead-generation + marketing-content — every AI-touched surface)
- **Signed regulator-facing attestation deliverable** (this Snapshot = internal-use PDF; Enterprise = signed statement suitable for state bar submission, malpractice-carrier renewal questionnaire, plaintiff-class production, or judicial-hearing exhibit)
- **On-site scoping session with your team** (this Snapshot = data-driven only; Enterprise = on-ground with your GC + managing partner + IT/CIO)
- **30/60/90 remediation roadmap w/ named owners + milestones**
- **Adaptive drift-monitoring implementation guidance**
- **Re-audit cadence recommendation tied to your E&O renewal cycle**

**Baseline Audit ($2,500 / 5 days) is the intermediate tier** — gap map + measurable test + 30/60/90 roadmap on ONE AI surface. Snapshot credit applies to Baseline OR Enterprise upgrade within 30 days.

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## 9. Independent-verifier declaration

I, Kevin Luddy, as principal of contrarianAI LLC, personally attest that:

1. contrarianAI is not an employee, contractor, equity holder, or vendor of the audited firm or any of its legal-AI vendors (Eve, Supio, Darrow, Harvey, Casetext, Aracor, Ivo, Callidus, or any similar).
2. Assessment tools (distributional-shape sensor, retrieval-auditor, tool-call-grader, prompt-drift-sensor, predictor-corrector, router-drift-sensor) use model families and evaluation methods distinct from those in production at the audited firm.
3. This Snapshot is based on the data-sample submitted by the buyer. Changes to the audited firm's infrastructure after the Snapshot date are outside this Snapshot's scope.
4. Underlying audit evidence is retained for the Snapshot horizon and will be made available for legitimate regulatory / audit / counterparty inquiry with appropriate legal process.

**Signed:** Kevin Luddy, Principal, contrarianAI LLC
**Date:** 2026-07-21
**Retention key:** `5e9c08934c26e1fff1fa3128`

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## 10. Charts + artifacts

- `chart_pf_distributional_drift.png` -- 7-day rolling mean triage-score per client cohort over the 90-day period
- `chart_pf_baseline_vs_drifted.png` -- Cohort C3 baseline vs recent triage-score distribution (largest observed drift)
- `chart_pf_lane_shift.png` -- Triage-lane rate per cohort, baseline vs recent
- `chart_pf_standard_vs_contrarianai.png` -- Standard case-management platform view vs contrarianAI independent-verifier view
- `pf_drift_analysis.json` -- machine-readable analysis + event metadata
- `pf_dataset.csv` -- synthetic 90-day case-intake input data
- `pf_agent_decisions.csv` -- case-intake AI output + scored triage decisions

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*End of Snapshot CAI-PF-GEN-DEMO-5e9c08934c26e1fff1fa3128*

**== END OF SAMPLE — YOUR ACTUAL $499 SNAPSHOT WILL LOOK STRUCTURALLY IDENTICAL BUT WITH YOUR FIRM'S DATA + AI SYSTEM + JURISDICTIONAL FOOTPRINT + REGULATORY-FRAMEWORK-CITATIONS ==**

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## To purchase YOUR firm's $499 Snapshot:

1. Visit: **contrarianai-landing.onrender.com/legal-snapshot.html**
2. Click **Buy $499** — Stripe direct-buy, no procurement approval
3. Complete intake form (~2 min): name your AI system + submit 100-1000 anonymized case-intake decision records
4. Report PDF delivered via email within 3 business days

**Snapshot credit ($499) applies to upgrade within 30 days:**
- **Baseline Audit** ($2,500 / 5 days) — Snapshot credit = $499 off
- **Enterprise Attestation** ($35-55K / 3-6 weeks) — Snapshot credit = $499 off

Book a call: **cal.com/kevin-luddy-0dlzuu**