Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Day 1: Establishing the Foundation — Ingest, Search, Retrieval
Module 1: The Legal Engineer’s Landscape
- Learning objectives — understand the role, where AI fits within legal work, and the two critical risks that permeate the field.
- Topics
-
- The legal-engineer role and its current market demand.
- Where AI fits: eDiscovery, review, contracts, research, investigations; explaining the EDRM model simply.
- Build vs. Buy decisions.
- The two overarching risks: confidentiality/privilege and defensibility.
Module 2: Legal Data Is Messy — Ingestion and Extraction
- Learning objectives — handle the reality of processing legal data at scale.
- Topics
- Managing 1,400+ file types, emails, PST files, scanned paper, load files (.dat/.opt); critical embedded metadata.
- Text extraction (Tika), OCR, and deduplication strategies.
- Lab: FreeEed Ingestion — construct an ingestion pipeline over a deliberately messy document set (emails/PST, scans, load files).
Module 3: Search and Retrieval — the Foundation
- Learning objectives — build the core eDiscovery primitive: finding anything within everything.
- Topics — full-text search and indexing (Solr/Lucene); relevance, metadata, and date filtering; searching across OCR-processed content.
- Lab: eDiscovery Search — index a corpus and execute real eDiscovery-style searches, including within OCR-processed scans.
Module 4: RAG for Legal Documents — with Citations
- Learning objectives — build RAG over legal documents that cites its sources.
- Topics
- Why retrieval, not fine-tuning, is preferred for sensitive material — the model never absorbs the documents directly.
- Chunking, embeddings, and crucially citations/provenance.
- Multi-document and thread summarization.
- Lab: Legal RAG with Citations — construct a RAG Q&A over a document set that answers queries with source citations.
Day 2: Making It Private, Defensible, and Shippable
Module 5: Privacy, Privilege, and Local Serving — The Privilege Trap
- Learning objectives — keep legal data on-site and capable of certification.
- Topics
- Where data actually goes when interacting with cloud AI.
- Privilege waiver, duty of competence, and the “private” spectrum (contractual vs. physical).
- Morgan v. V2X and why local deployment is court-defensible.
- Serving local models (Ollama/vLLM) and monitoring outbound traffic.
- Lab: Local Model + Egress Proof — run a local model end-to-end and prove, via monitoring, that no data left the environment.
Module 6: Defensible AI Review
- Learning objectives — measure and document an AI review so it withstands legal challenge.
- Topics
- The numbers that hold up in court: recall, elusion, precision, ground-truth validation; TAR/active learning.
- Transparency (why did it classify this document?) and reproducibility — pin the model version, fix settings, log everything.
- The “defensible case snapshot” enabling someone to re-run your review a year later and obtain identical results.
- Lab: Defensible Review — measure an AI review against blind ground truth and produce a reproducibility bundle.
Module 7: Ship It — Workflow, Private Deployment, and Governance
- Learning objectives — assemble components into a workflow, deploy privately, and assess performance.
- Topics
- A multi-step legal workflow (ingest → search → summarize → review → produce) with human-in-the-loop oversight.
- Private/on-premises deployment essentials (containerization; keeping data within the building).
- AI governance for legal in brief, and scoring the system using SAIS-100 (the Elephant Scale Secure AI Score).
- Lab: Score and Package — wire a multi-step workflow, score it with SAIS-100, and package it for private deployment.
Capstone (integrated across Day 2)
- Construct a private, defensible legal-AI application end-to-end — ingest a messy corpus, search it, answer questions with citations using a local model, measure a defensible review, and package for private deployment.
- Participants leave with a portfolio project that mirrors the actual legal-engineer role.
Optional Day 3 / Advanced Modules (deliverable as a 3rd day or modular series)
- Investigations: Entities, Relationships, and Timelines — extract people/organizations/dates, reconstruct email threads, build chronologies, map near-duplicates and document lineage. Lab: build a timeline and entity/relationship view.
- Agentic and Multi-Step Legal Workflows (deep) — richer orchestration, contract analysis, multi-document synthesis, tool use, and guardrails as design principles. Lab: build a multi-step workflow with a human checkpoint.
- Deployment at Scale — on-premises and appliance deployment, distributed processing for large volumes, regulated environments (CJIS, government, higher-ed), hardware sizing. Lab: containerize and scale a processing job across workers.
- Governance and Compliance Deep-Dive — the AI-regulation landscape (100+ US state AI laws, EU AI Act), audit requirements, and a full SAIS-100 governance audit. Lab: audit a legal-AI system against a governance/defensibility checklist.
Requirements
- Proficiency in Python and basic APIs.
- Helpful: Familiarity with Large Language Models (LLMs) at a user level (no ML background required — we construct the conceptual framework).
- No legal background necessary — essential legal concepts are taught within context.
Audience
- Software and AI engineers transitioning into legal technology.
- Engineers at legal-tech companies requiring deeper domain knowledge in law.
- Tech-savvy legal, eDiscovery, or information-governance professionals who prefer building solutions over purchasing them.
- Individuals aiming for “legal engineer” or “AI legal engineer” roles.
14 Hours
Testimonials (1)
That i gained a knowledge regarding streamlit library from python and for sure i'll try to use it to improve applications in my team which are made in R shiny
