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: Build the Foundation — Ingest, Search, Retrieve
Module 1: The Legal Engineer’s Landscape
- Learning objectives — understand the role, where AI fits into legal work, and the two risks that permeate everything.
- Topics
-
- The legal-engineer role and why it is currently in demand.
- Where AI fits: eDiscovery, review, contracts, research, investigations; the EDRM model explained simply.
- Build versus buy decisions.
- The two risks that run through everything: confidentiality/privilege and defensibility.
Module 2: Legal Data Is Messy — Ingestion and Extraction
- Learning objectives — handle the reality of legal data at scale.
- Topics
- Over 1,400 file types, email and PST, scanned paper, load files (.dat/.opt); embedded metadata that matters.
- Text extraction (Tika), OCR, and de-duplication as strategic choices.
- Lab: FreeEed Ingestion — build an ingestion pipeline over a deliberately messy document set (email/PST, scans, load files).
Module 3: Search and Retrieval — the Foundation
- Learning objectives — build the core eDiscovery primitive: finding anything inside everything.
- Topics — full-text search and indexing (Solr/Lucene); relevance, metadata and date filtering; searching across OCR’d content.
- Lab: eDiscovery Search — index a corpus and run real eDiscovery-style searches, including inside OCR’d 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 ingests the documents directly.
- Chunking, embeddings, and above all citations/provenance.
- Multi-document and thread summarization.
- Lab: Legal RAG with Citations — build a RAG Q&A over a document set that answers with source citations.
Day 2: Make It Private, Defensible, and Shippable
Module 5: Privacy, Privilege, and Local Serving — the Privilege Trap
- Learning objectives — keep legal data local and able to certify it.
- Topics
- Where data actually goes when it hits a cloud AI.
- Privilege waiver, duty of competence, and the “private” spectrum (contractual vs. physical).
- Morgan v. V2X and why local hosting 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 exited the environment.
Module 6: Defensible AI Review
- Learning objectives — measure and document an AI review so it holds up in court.
- Topics
- The numbers that stand up in court: recall, elusion, precision, ground-truth validation; TAR/active learning.
- Transparency (why did it code this document?) and reproducibility — pin the model, fix settings, log everything.
- The “defensible case snapshot” that allows someone to re-run your review a year later and achieve the same result.
- Lab: Defensible Review — measure an AI review against a blind ground truth and produce a reproducibility bundle.
Module 7: Ship It — Workflow, Private Deployment, and Governance
- Learning objectives — assemble the pieces into a workflow, deploy it privately, and score it.
- Topics
- A multi-step legal workflow (ingest → search → summarize → review → produce) with human-in-the-loop.
- Private/on-premises deployment essentials (containerize; keep data within the building).
- AI governance for legal in brief, and scoring the system with 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)
- Build a private, defensible legal-AI application end to end — ingest a messy corpus, search it, answer questions over it with citations using a local model, measure a defensible review, and package it for private deployment.
- Participants leave with a portfolio project that represents the legal-engineer role.
Optional Day 3 / Advanced Modules (deliverable as a 3rd day or a modular series)
- Investigations: Entities, Relationships, and Timelines — extract people/orgs/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-doc synthesis, tool use and guardrails as a design principle. 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, the 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 LLMs at a user level (no ML background required — we build the conceptual model).
- No legal background required — necessary legal concepts are taught in context.
Audience
- Software or AI engineers transitioning into legal tech.
- Engineers at legal-tech companies who need deeper domain knowledge.
- Technically-minded legal, eDiscovery, or information-governance professionals who wish to build solutions rather than just purchase them.
- Anyone targeting the “legal engineer” or “AI legal engineer” role.
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
