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Legal experience · AI systems engineering

The AI-native law firm, built in practice.

Legal work redesigned as a measurable system — from the matter workflow to the review gate, the evaluation set and the governance around it.

System architecture Live model
01Matter input
05 linked stages02 review gates01 governed system

Firms don't become AI-native by buying tools.

They become AI-native by changing how work is produced — workflows, infrastructure, governance, skills, economics. That change can be engineered. This site shows how.

Working model

A matter is a system. Make every decision visible.

Active stage

Diagnose

Map the actual matter workflow before choosing technology.

System signalInput → decision → output
  • Task boundary
  • Human sign-off
  • Cost of error
The difference

Accuracy is an evidence question, not a vendor claim.

Every system published here shows its architecture, the evidence available, the evidence still missing and the failure modes that matter.

Worked example

How a threshold gets set.

Performance is scored against verified answers before it enters a claim. Hover a point to read the pair it came from.

Name-match threshold, measured
Hover a point to read the pair
Genuine matches scored 76–100, genuine mismatches 34–38. The threshold sits at 60, in the empty gap, set low on purpose: a false alarm on a legitimately shortened caption is how a tool gets switched off.
Published evidence

The work, documented.

Insights

Practical guides for lawyers, and what it takes for a firm to change how the work is produced.

  1. 01A first draft from your own precedentsDrafting · 45 minutes
  2. 02Checking citations before you rely on themResearch · 20 minutes
Case studies

Systems published with their architecture, operating decisions, available evidence and failure modes.

  1. 01Reply triage and client routingClient engagement
  2. 02An agent that negotiates meeting times and books themClient engagement

Bring the workflow that needs to change.

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