Litigation Engineering
Litigation Engineering is the discipline of building software systems where the outputs become evidence. It's what happens when you apply engineering rigor to domains where errors have seven-figure consequences, where opposing counsel will scrutinize every decision your system made, and where "it usually works" isn't good enough.
Why This Exists
Most software engineering operates under a familiar motto: move fast and break things. Ship it, iterate, fix it in the next sprint. That philosophy works when the cost of failure is a bad review or a lost click.
But a growing category of software doesn't get that luxury. AI systems are producing outputs that become evidence in litigation. Data pipelines are generating numbers that determine liability. Automated workflows are making decisions that end up in court filings, regulatory submissions, and expert testimony. When your system's output is Exhibit A, "we'll fix it in the next release" isn't an option.
Litigation Engineering is the practice of building for that reality. Move deliberately. Audit everything. Ensure reproducibility. Because in an adversarial environment, every shortcut you took becomes opposing counsel's favorite question.
Core Principles
Chain of Custody
Every transformation your system performs is a logged, timestamped, immutable event. Document ingestion, text extraction, AI processing, human review — each step in the chain is recorded so the provenance of any output can be traced back to its source.
Auditability
Every output your system produces must be explainable. That means pinning model versions, logging prompt templates, and storing the exact inputs that produced each result. "We ran it through GPT" is not an audit trail.
Reproducibility
Given the same inputs and the same model version, your system should produce the same result. If it can't, you need to know why, and you need to be able to explain that variance under oath.
Human-in-the-Loop
AI can surface, sort, and summarize. Humans decide, verify, and certify. This boundary must be enforced in architecture, not just policy. If your system allows an AI output to become part of a legal filing without human review, you're building a liability.
Where It Applies
Any domain where software outputs face adversarial scrutiny:
- Legal tech and e-discovery pipelines
- AI systems whose outputs become evidence
- Regulatory compliance and financial reporting
- Healthcare and life sciences data systems
- Insurance claims processing and fraud detection
The common thread: when someone with a law degree and a motive will examine your system's work product, you need engineering practices that hold up under that scrutiny. For legal tech developers and AI engineers, this isn't optional — it's the job.
FAQ
Have questions? See the Litigation Engineering FAQ for answers to common questions about chain of custody, reproducibility, human-in-the-loop design, and more.
The Deep Dive
For the full technical breakdown — pipeline architecture, verification layers, and lessons learned from building these systems in production — read the companion post: Litigation Engineering: When AI Meets High Stakes.