Litigation Engineering: When AI Meets High Stakes

Building legal-tech pipelines that hold up under pressure. Things have to be different when the stakes are life and death.

Litigation Engineering Legal Tech AI

TL;DR: Legal tech isn’t like other software. When your system’s output becomes evidence, when errors have seven-figure consequences, when opposing counsel will scrutinize every decision, you build differently. This is what I’ve learned about engineering for litigation.

Most software can afford to be wrong sometimes. A product recommendation that misses the mark costs you a click. A search result that’s slightly off costs you a few seconds. In litigation, a wrong output can cost your client millions of dollars, or worse.

Legal tech operates in an adversarial environment. Opposing counsel will scrutinize your system’s decisions. Judges will ask how you arrived at a conclusion. Regulators will want an audit trail. Your outputs aren’t just data; they’re evidence. And evidence that can’t be explained or reproduced is evidence that gets thrown out.

This changes everything about how you build. In most software, “move fast and break things” is a philosophy. In litigation engineering, “break things” means malpractice.

Where AI Helps and Where It Can’t

AI is genuinely powerful for the work that buries legal teams: triage, document clustering, summarization, and pattern recognition across massive corpora. A review that used to take a team of associates six months can be done in weeks with the right pipeline. That’s real value.

But AI cannot replace legal judgment. It cannot guarantee zero hallucinations. It cannot sign an affidavit. And in a domain where a single fabricated citation can end a career (ask the lawyers who submitted ChatGPT-generated case law to a federal judge), the line between “AI-assisted” and “AI-decided” is a line you cannot afford to blur.

The rule I follow: AI can surface, sort, and summarize. Humans decide, verify, and certify. If your system doesn’t enforce that boundary, you’re building a liability, not a tool.

Build for Defensibility

Chain of custody. Every transformation your system performs on a document should be a logged, timestamped event. If you extract text from a PDF, that extraction is an event. If you run it through an LLM for summarization, that’s an event. If a reviewer approves the summary, that’s an event. The chain should be complete and immutable.

Auditability. Every output your system produces should be reproducible. Given the same inputs and the same model version, you should get the same result. This means pinning model versions, logging prompt templates, and storing the exact inputs that produced each output. “We ran it through GPT” is not an audit trail.

Verification layers. Schema validation on every structured output. Confidence scoring on every extraction. Spot checks built into the workflow, not bolted on after. And attorney review gates at every point where an AI output could become part of a legal filing. These aren’t speed bumps; they’re the reason your system holds up in court.

The Pipeline in One Page

A litigation engineering pipeline, stripped to its essentials, has four stages:

Intake. Documents come in messy. Scanned PDFs, email threads, spreadsheets, photos of whiteboards. The intake stage classifies, normalizes, and routes. Bad inputs get flagged early, not after they’ve contaminated downstream processing.

Process. This is where AI earns its keep. Extraction, classification, summarization, cross-referencing. Every step produces structured output validated against a schema. Every step logs its inputs, outputs, and the model version used.

Review. Humans verify what the system produced. Not all of it, but a statistically meaningful sample, plus anything the system flagged as low-confidence. Review decisions are logged the same way processing decisions are.

Produce. Final outputs are generated from reviewed, verified data. They carry a complete provenance chain from source document to final output. If anyone asks “where did this number come from,” the answer is one query away.

The design philosophy is simple: trust nothing, log everything, and make every decision reversible until a human says otherwise.

Key Takeaways

  • In litigation, your system’s output is evidence. Build accordingly.
  • AI should surface and sort. Humans should decide and certify. Enforce the boundary in architecture, not policy.
  • Chain of custody isn’t a nice-to-have. It’s the foundation everything else rests on.
  • Reproducibility is non-negotiable. Pin your models, log your prompts, store your inputs.
  • The systems that hold up under scrutiny are the boring ones: strict validation, complete logging, and human review at every critical juncture.