Build a LAT parse session from a customer's applicable laws. Applies all necessary filters (in-force, not-revoked, missing LAT, making classification) before creating the session to avoid wasting parse effort on laws that don't need processing.
Safe procedures for modifying the database schema. Covers the uk_lrt view + INSTEAD OF trigger pattern, column type changes on partitioned tables, and migration best practices.
Create a scrape session programmatically from data (CSV, list of law names, query results). Handles session + record creation with correct schema constraints so records appear in the admin UI.
Sync a customer's legal register, duties, and actor tuples to Baserow. Covers the three-table model, significance filters, sync config, and common issues.
Run `mix customer.pipeline_status` to report a customer's law corpus readiness — from applicability through LAT parsing, fractalaw enrichment, to Baserow-ready governed duties.
Generate a data quality report comparing a customer's legacy vendor register (Enhesa, Nimonik, etc.) against SertantAI's enriched legal database. Produces confusion matrix, revoked law analysis, coverage gaps, duty density, and family distribution. Outputs .md report + .csv appendices. Designed for customer senior management.
Import a customer's legacy legal register CSV (Enhesa, Nimonik, etc.), transform and match against LRT, group results into matched/scrapeable/not-handled, and create a scrape session for missing laws. First phase of customer onboarding.
Human-AI partnered workflow for LAT (Legal Article Text) parsing sessions. Guides making-classification review, monitors parse progress, runs post-parse QA (LAT shape, hierarchy integrity, annotation sanity), triggers taxa enrichment via Zenoh, then promotes data through NAS sync and production sync with QA gates at each stage.