title
Please take a moment to fill out this form. We will get back to you as soon as possible.
All fields marked with an asterisk (*) are mandatory.
Legal AI Engineering
Duration
3 Days
Course
LEGAL-AI-ENG-2026
Available Formats
Classroom Training, Online Training
Course Description
Overview
- Course goals
- Turn software / AI engineers into legal engineers — the people who can actually build AI for legal work (eDiscovery, review, investigations).
- Teach participants to build legal-AI systems that are private (nothing leaves the building), defensible (you can prove what they did), and deployable on-prem — the exact skills legal-tech firms and law departments are hiring for.
- Do it in two intensive, hands-on days — the whole build path, no fluff.
- In two days, participants build the full core stack: ingest and extract messy real-world legal data, search and retrieve it, add retrieval-augmented generation (RAG) with citations, keep it private on a local model and prove it, run a defensible AI review with court-ready metrics, and package the result for deployment. An optional Day 3 / modular deep-dive extends into investigations, agentic workflows, and on-prem scale.
- Special emphasis is placed on:
- Confidentiality and privilege — where legal data actually goes, and how to keep it local and certifiable.
- Defensibility — measuring, documenting, and reproducing an AI review so it holds up.
- Applying all concepts to a real-world system: FreeEed (open-source eDiscovery platform with AI capabilities) as the continuous hands-on lab.
Format
- Instructor-led, highly hands-on (learn by doing)
- Every module has a working lab; the labs compound into a real legal-AI application by the end
- Taught from the builder’s seat — the instructor ships a real legal-AI platform (FreeEed) and teaches AI security, so the labs are production reality, not toy demos
Lab environment
- A modern laptop and reliable internet access
- A local model runtime (Ollama / vLLM, OpenAI-compatible) for the privacy and local-serving labs
- The FreeEed lab environment for legal-data ingestion, search, and review labs
- Deliberately messy sample legal corpora are provided
Objectives
- After the two-day core, participants will be able to:
- Ingest and process messy real-world legal data (1,400+ file types, email/PST, scans, load files).
- Build search + RAG over documents that cites its sources and keeps data local.
- Choose local vs. cloud AI defensibly, and prove nothing left the machine.
- Measure and document an AI review so it holds up in court (recall, elusion, precision, ground truth).
- Package a private, defensible legal-AI application and score it with the SAIS-100 (Elephant Scale Secure AI Score) rubric.
- The optional Day 3 / deep-dive adds: investigative AI (entities, relationships, timelines), agentic multi-step legal workflows, and on-prem deployment at scale in regulated environments.
Audience
- Software / AI engineers moving into legal tech
- Legal-tech company engineers who need legal-domain depth
- Technically-minded legal / eDiscovery / information-governance professionals who want to build, not just buy
- Anyone targeting the “legal engineer” / “AI legal engineer” role
Prerequisites
- Comfortable with Python and basic APIs
- Helpful: familiarity with LLMs at a user level (no ML background required — we build the mental model)
- No legal background required — the legal concepts you need are taught in context
Topics
Day 1: Build the Foundation — Ingest, Search, Retrieve
- Module 1: The Legal Engineer’s Landscape
- Learning objectives — understand the role, where AI fits in legal work, and the two risks that run through everything.
- Topics
- The legal-engineer role and why it is being hired right now
- Where AI fits: eDiscovery, review, contracts, research, investigations; the EDRM model in plain terms
- Build vs. buy
- 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
- 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 a choice
- 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: find anything inside everything.
- Topics — full-text search and indexing (Solr/Lucene); relevance, metadata and date filtering; search 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, for sensitive material — the model never swallows the documents
- 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
- Module 5: Privacy, Privilege, and Local Serving — the Privilege Trap
- Learning objectives — keep legal data local and be able to certify it.
- Topics
- Where the 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 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, with monitoring, that no data egressed
- Module 6: Defensible AI Review
- Learning objectives — measure and document an AI review so it holds up.
- Topics
- The numbers that hold 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 the settings, log everything
- The “defensible case snapshot” that lets someone re-run your review a year later and get 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-prem deployment essentials (containerize; keep the data in 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
- 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 is the legal-engineer job.
- 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-prem 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.
- Evaluation — capstone project, lab completion, and class participation
- Requirements — minimum attendance, successful completion of the capstone, completion of module labs
- Certificate Awarded — Legal AI Engineer Certificate
- This is the legal-vertical track of the Elephant Scale Secure AI Learning Path.
- It pairs naturally with Building Secure AI Applications (builders) and AI Security for Security Teams (defenders), and applies the SAIS-100 framework in its governance work.
- FreeEed (open-source eDiscovery + AI) is the hands-on lab throughout — the real system that keeps the labs production-grade, not slideware.
Related Courses
-
GPT-3
PLPJ-215- Duration: 2 Days
- Delivery Format: Classroom Training, Online Training
- Price: ???
-
50 Algorithms Every Programmer Should Know
PLPJ-220- Duration: 4 Days
- Delivery Format: Classroom Training, Online Training
- Price: 2,340.00 USD
Self-Paced Training Info
Learn at your own pace with anytime, anywhere training
- Same in-demand topics as instructor-led public and private classes.
- Standalone learning or supplemental reinforcement.
- e-Learning content varies by course and technology.
- View the Self-Paced version of this outline and what is included in the SPVC course.
- Learn more about e-Learning
Course Added To Shopping Cart
bla
bla
bla
bla
bla
bla
Self-Paced Training Terms & Conditions
??spvc-wbt-warning??
Exam Terms & Conditions
??exam-warning??
??group-training-form-area??
??how-can-we-help-you-area??
??personalized-form-area??
??request-quote-area??
Sorry, there are no classes that meet your criteria.
Please contact us to schedule a class.
STOP! Before You Leave
Save 0% on this course!
Take advantage of our online-only offer & save 0% on any course !
Promo Code skip0 will be applied to your registration
Nothing yet
Purchase Information
??elearning-coursenumber?? ??coursename??
View Cart
title
Please take a moment to fill out this form. We will get back to you as soon as possible.
All fields marked with an asterisk (*) are mandatory.