hide from accountability.
That map is what I sell.
My practice was built in the field — not in a consulting firm. Twelve years inside the Defense Logistics Agency taught me how federal data infrastructure works and how institutional actors use procedural complexity to evade oversight. Three years running Clutch Justice, Michigan’s independent accountability journalism platform, taught me how to document it in real time. A pro se civil rights case in Barry County district court taught me what happens when you’re on the other side of that machinery without counsel — and how to build a record anyway.
That combination — federal insider, investigative analyst, litigation-tested — is unusual. Most consultants in this space have one of those vantage points. I have all three, and I’ve published the work that proves it.
I work with organizations that can’t afford to be wrong about institutional risk: legal teams doing accountability litigation, litigation finance firms assessing case quality, legal tech companies building for the courts ecosystem, and watchdog organizations that need someone who has been inside the docket, not just adjacent to it.
Three Tracks, One Practice
Each track draws on the same core skill: building evidentiary records of institutional behavior that hold under scrutiny.
Government Accountability & Institutional Forensics
I have mapped procurement networks, campaign finance flows, and intergovernmental accountability gaps at county and state level in Michigan. I understand how public actors use bureaucratic architecture to obscure liability — and how to build an evidentiary record that surfaces it. This work is grounded in primary sources: court records, public filings, FOIA responses, campaign finance data, and document chain-of-custody that meets publication standards.
- Document trail analysis & public records strategy
- Institutional network mapping
- Civil rights exposure assessment
- MMRMA / public insurer liability identification
- FOIA strategy & response analysis
- Campaign finance & procurement network analysis
- Civil rights litigation support teams
- Watchdog & accountability journalism orgs
- Legal reform nonprofits
- Policy advocacy organizations
- Government affairs & compliance teams
Procedural Abuse Pattern Recognition
Vexatious filing, pretextual enforcement, and procedural manipulation follow identifiable signatures. I have documented these patterns in active Michigan district court litigation — and published the analysis publicly. That pattern recognition is a transferable skill with direct ROI for organizations that need to assess litigation risk, identify bad-faith actors, or build early-warning systems for process abuse. I have been on the receiving end of these tactics and came out with receipts.
- Case file forensic review
- Procedural abuse signature identification
- Litigation risk pre-screening
- Bad-faith actor pattern analysis
- Expert framing for legal reform advocacy
- Pre-litigation institutional exposure assessment
- Litigation finance firms
- Insurance SIU & legal ops teams
- Court reform organizations
- Defense litigation teams
- Plaintiff-side civil rights attorneys
Legal AI & Court Systems Domain Expertise
Most companies building AI products for the legal market have strong engineering teams and limited domain depth. They understand NLP. They don’t always understand how a clerk’s office actually works, how procedural abuse gets constructed filing by filing, or how cases die quietly in ways that never surface in training data. I do. I also build LLM-powered workflows and have published on AI ethics and institutional accountability. That combination — domain expert who can also build — is uncommon.
- Court systems domain advisory
- LLM workflow development & implementation
- Product red-teaming for court-adjacent tools
- Training data quality & bias review
- AI ethics consultation
- Subject matter expert engagement
- Legal AI startups & scaleups
- E-discovery & legal ops firms
- Court technology vendors
- Law school & research institutions
- Public interest tech organizations
The Fit-Bench Act
Michigan courts operate on a binary model: a judge is either licensed or removed. No mechanism exists for early detection, temporary relief, or structured transition when capacity declines. The Fit-Bench Act proposes a three-layer confidential assessment system — baseline evaluation at entry, monthly performance data capture using existing court systems, and a triggered confidential review when statistical thresholds are crossed.
This is what original policy work built on investigative infrastructure looks like. Draft statutory language, a functional web-based scorecard application, and briefing materials are available on request.
Read the full framework →Engagement Models
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