Home → Module Library
The operational capability layer of the Legal AI OS.
Modules are the institutional artefacts a legal function runs to advance maturity, produce Defensibility evidence, and operate on canonical ground in front of a regulator or a board. Anchored across the 8 Pillars and 6 Operating Layers. Methodology-versioned. Editorially independent.
PILLAR
AUDIENCE
+ More filters– Fewer filters2 active
11 Modules
COT-01
Continuous Optimization Cycle
Systematically capture AI performance feedback, map signals to Risk Taxonomy 2026 classes, and execute ROAI-attributed optimization cycles across the legal department's AI portfolio.
Module
Initial setup 2–4 weeks; continuous telemetry; monthly analysis; quarterly planning and review (0.5–1 day per quarter).
View →
DAT-02
Data Inventory & Classification Methodology
Classify every data asset by sensitivity level, assign AI processing permissions by classification, and build the governance record that proves Defensible AI adoption.
Module
Initial build 4–6 weeks; quarterly updates 1–2 days; annual review 3–5 days
View →
DAT-04
Data Minimization Methodology
Apply data minimization across every stage of the AI data lifecycle, govern shadow AI through the canonical Class 6 protocol, and build the evidence record that proves Defensible AI.
Module
Initial rollout 4–6 weeks; continuous monitoring with monthly and quarterly checkpoints
View →
INS-01
AI Liability & Insurance Posture
Assess professional liability exposure, insurance coverage gaps, and vendor indemnification adequacy for AI tools across the Risk Taxonomy 2026 nine-class framework.
Module
1–2 days initial per tool; 2–4 hours per tool at renewal or quarterly review
View →
MAT-01
Maturity Grid
The canonical Year-1 maturity instrument — plots organisational Adoption against AI Sophistication on a 5×4 grid to produce a defensible quarterly position and Maturity Stack-linked progression evidence.
Module
1–2 days for baseline; 0.5 day per quarterly review; 1 day annual strategic refresh
View →
MAT-02
Legal AI Readiness Baseline
The canonical 8-pillar readiness diagnostic that captures baseline maturity across all Legal AI OS transformation dimensions and maps current position to the MAT-01 2D maturity grid.
Module
1–2 days for full baseline; 0.5 day for quarterly review; 1 day for annual re-baseline
View →
MAT-03
Maturity Gap Analysis
Structured 6-step gap analysis to map current vs. target Legal AI maturity, risk exposure, and roadmap.
Module
2–4 days for baseline; 1 day for quarterly and annual refreshes
View →
SUS-01
Vendor Performance Review Cycle
Evaluate AI vendor performance quarterly across five weighted dimensions with Risk Taxonomy 2026 mapping and Agentic Tier governance checks.
Module
3–4 hours per vendor per quarter once established
View →
SUS-03
AI Market Scan Radar
Systematically monitor the evolving legal AI market quarterly — tracking vendor developments, funding events, and emerging technology innovations — with Risk Taxonomy 2026 vendor risk assessment and Agentic Tier evaluation criteria for Level 4 tool announcements.
Module
Quarterly scan cycle: 4–6 hours for structured analysis; monthly trend monitoring: 1–2 hours
View →
SUS-04
Vendor Exit Methodology
Plan and execute legal AI vendor exits — from lock-in risk assessment through data migration and contract termination — with Risk Taxonomy 2026 exit trigger classification and Agentic Tier shutdown protocol for Level 4 tools.
Module
Annual review: 4–6 hours per vendor; triggered exit execution: 8–20 weeks depending on integration complexity
View →
TAL-03
AI Champion Network Guide
Continuous-operation guide for selecting, training, and deploying AI Champions who accelerate peer adoption, verify AI BoM compliance before tool advocacy, and execute the Class 6 Shadow AI Champion Protocol as the legal department's first-line detection network.
Module
Initial design and launch: 4–6 weeks; ongoing operation: 4–6 champion hours per month plus quarterly reviews.
View →
Advisory
The full operating system in one Programme.
Programme Design and Strategic Retainer engagements operate the canonical Module sequence end-to-end — Defensibility evidence produced, Maturity progression evidenced quarterly, methodology version pinned. The Module Library is the artefact; the engagement is the operating posture.
View Engagement Models