FMEA is the structured methodology engineers use to prevent failures, from chipsets to data centers to rockets. It was built for machines. Litigation has humans, with wounds, biases, and escalation patterns that amplify every failure mode. ARPN adapts FMEA for adversarial human systems, starting left of boom.
For litigators, law students, and anyone who needs to predict what the other side will do next. Built by an engineer who learned failure analysis across fraud detection, hyperscale cloud infrastructure, and manufacturing, then applied it to litigation.
Three approaches. Two missing dimensions. One framework that closes the gap.
FMEA prevents failures in the most complicated systems on earth. ARPN applies that discipline to the most unpredictable system: adversarial humans. We added two dimensions, Behavioral Amplifier and Cascade Reach, so the scoring captures what people actually do under stress, not what rational actors should do.
Five factors. One score. A complete picture of adversarial risk.
Five tiers of behavioral context that power the BA dimension, from personality baseline to system-level dynamics.
T4 and T5 are the dimensions most commonly overlooked in traditional risk assessment, and in our validation data, they produced the highest-impact findings.
The behavioral economics that power the BA dimension. Each is decades-established. What’s new is applying them systematically to litigation failure modes.
These aren’t speculative. Each is backed by decades of behavioral economics research (Kahneman, Tversky, Thaler). What’s new is applying them systematically to litigation failure modes through the BA dimension.
A structured adversarial analysis process. Find your vulnerabilities before they do. Find theirs before they hide them.
A real appellate case, scored with ARPN. Every number is verifiable against the published opinion.
Two-member Texas LLC (L&S Pro-Line), oilfield services. Burkett held 75%, Gagliano held 25%. The Company Agreement required consent for expenditures over $5,000 and included a Push-Pull buyout provision. What started as a business disagreement escalated into a multi-year war. The jury returned a $32M verdict, including $15.1M in punitive damages for malice. On appeal, the Ninth Court affirmed in part but reversed key damage awards and remanded for further proceedings.
Analytical perspective: This example scores from a trial-level perspective: what could counsel have predicted before the verdict? We score individual failure modes (evidence items, procedural events, behavioral patterns). An appellate-level analysis of the same case surfaces architecture-level failures (threshold legal rulings, damages segmentation, theory selection) with higher Cascade Reach scores and often Critical-tier findings. Both perspectives are valid. Choose yours before you score.
| Rank | ID | Failure Mode | S | O | D | BA | CR | ARPN | Tier |
|---|---|---|---|---|---|---|---|---|---|
| 1 | BT-2 | Push-Pull buyout declared valid, loss of standing | 8 | 5 | 3 | 1.2 | 2 | 288 | Low |
| 2 | BT-1 | CFO duty negligence undermines counterclaim credibility | 6 | 7 | 4 | 1.0 | 1 | 168 | Low |
| 3 | BT-3 | Jury believes unauthorized payments narrative | 7 | 4 | 5 | 1.0 | 1 | 140 | Low |
| Rank | ID | Failure Mode | S | O | D | BA | CR | ARPN | Tier |
|---|---|---|---|---|---|---|---|---|---|
| 1 | RT-5 | Bribery + sex offender hiring: jury finds malice | 9 | 7 | 4 | 2.0 | 1 | 504 | Moderate |
| 2 | RT-2 | $525K self-dealing proven by bank records | 9 | 8 | 2 | 1.7 | 2 | 489.6 | Moderate |
| 3 | RT-4 | CPA withdraws citing “suspicious activities” | 8 | 9 | 2 | 1.2 | 2 | 345.6 | Low |
| 4 | RT-3 | Commitment escalation: 5 amended petitions | 7 | 8 | 3 | 1.7 | 1 | 285.6 | Low |
| 5 | RT-1 | Physical exclusion destroys credibility with jury | 8 | 9 | 1 | 1.5 | 2 | 216 | Low |
Standard RPN (S × O × D) would have rated RT-5 at 252 (LOW tier). ARPN rates it 504 (MODERATE) because the BA=2.0 captures Burkett’s escalating pattern of behavioral failure. The jury agreed: they found malice and awarded ~$15.1M in punitive damages. The behavioral amplification was real and measurable.
What ARPN would have told Burkett’s counsel:
The thesis: After scoring, look for the pattern. In L&S v. Gagliano, the trial-level thesis is: behavioral escalation drove a majority owner past the point of rational decision-making. The BA dimension captures this; standard RPN cannot. An appellate-level thesis would be different: threshold legal architecture errors contaminated the entire case structure. Same case, different perspective, different insight.
All facts in the worked example above come from these public court records. Download them to verify the scoring or use them to practice the ARPN framework yourself.
Full docket (62 documents): Texas Courts: Case 09-21-00178-CV →
You’ve learned the framework. You’ve seen it applied. Now use it. The PDF worksheet has everything you need: blank FMEA tables, the scoring scales, and AI agent context so your assistant can help you score.
6 pages. Blank FMEA tables, scoring scales, 12-bias catalog, and AI agent context for assisted scoring.
Using an AI assistant? Load the PDF worksheet and your case documents into a conversation. Then type:
Use the attached worksheet and case documents to analyze this case.
The embedded agent context will guide your AI through scoring, thesis generation, and, if you keep going, counsel briefs, mandate matrices, remand checklists, and even draft court orders. In validation testing, this single prompt produced nine attorney-grade deliverables from a public case record.
These results are from Acquit.ai’s own prediction tracking system, not the case study above. Predictions are logged before outcomes occur. No post-hoc fitting. No cherry-picking.
Every prediction is logged before the outcome occurs. No post-hoc fitting. No cherry-picking. The framework is tested against real-world litigation dynamics where the stakes are maximum and the actors are under extreme stress.
The methodology was developed under live-fire conditions, not in a laboratory.
You just scored 8 failure modes manually. Imagine scoring hundreds, across multiple concurrent disputes, with automated calibration that improves with every prediction. Then generating a one-page counsel brief that translates those scores into leverage, risk, settlement posture, and recommended moves with deadlines. That’s what the pipeline produces. And it starts left of boom. ExhibitCTL collects and hashes your evidence before you know you’ll need it.
The ARPN Framework powers the Acquit Score™. From manual scoring to automated case intelligence. See it applied to your case data.
Get Your Acquit ScoreARPN Framework and Sentient Analysis Methodology © 2026 Colin McNamara / Acquit.ai. Licensed under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). Attribution required for academic and professional use. Commercial licensing: colin@acquit.ai