🇪🇺 1. How Litigation Lawyers Get Case Information in Europe
Litigation information usually falls into four categories:
A. Public Case Information (courts, decisions, filings)
In Europe, public court data is fragmented because each country has its own judiciary.
However, litigation lawyers typically use:
1. National Court Portals
Each country has digital case registers, e.g.:
Germany → Gerichtsentscheidungen, RIS (for Austria)
France → Legifrance (Cour de cassation + Conseil d’État decisions)
UK → BAILII / The National Archives
Netherlands → Rechtspraak.nl
Sweden → Domstolsverket
EU-wide → EUR-Lex, Curia
Lawyers check these to find:
Prior judicial decisions
Case histories
Docket information
Court rules / procedural guidelines
B. Paid Legal Research Tools
Most litigation teams rely heavily on commercial databases:
LexisNexis (UK, FR, DE, NL, EU case law)
Westlaw / Thomson Reuters
Wolters Kluwer (Kluwer Arbitration, Kluwer IP)
Beck-Online (Germany)
La Ley / Aranzadi (Spain)
Juris (Germany)
These provide:
Historical case law
Annotations & commentary
Key-number systems
Cited-by relationships
Precedent summaries
C. Information from Court Procedures (ongoing case)
Litigators obtain case-specific information from:
1. Court filings
Statements of claim
Defences
Witness statements
Exhibits / documentary evidence
Court orders
2. Opponent disclosures ("Discovery" or "Disclosure")
Varies by country.
EU systems usually use limited disclosure, except UK.
3. Client-provided materials
Contracts
Email trails
Business records
Internal memos
4. Expert reports
Engineering, medical, forensic, accounting experts may provide opinions.
D. Cross-border/EU information sources
For litigation involving Europe:
ECRIS → European Criminal Records Information System
BRIS → Business Registers Interconnection System (for company info)
EUIPO / EPO → Trademark/patent disputes
Financial institutions for AML/KYC
Police/Prosecutor databases (country-specific and not open to public)
🧠 2. How AI System Can Access and Use These Sources
AI cannot “scrape” or “hack” restricted systems.
But it can integrate with:
✔ Public databases (via APIs or scraping where allowed)
✔ Paid legal databases (if vendors provide API access)
✔ User-uploaded litigation materials
✔ Document Management Systems (DMS)
✔ Email / Outlook
✔ eDiscovery platforms
Ideal workflow for litigation AI:
1. Document ingestion
Court filings
Emails
Evidence
Expert reports
PDFs scanned → OCR → text
Index everything in a vector DB
2. RAG over litigation corpus
“Ask” questions like:
“Summarize the opposing party’s defence.”
“List all references to Contract A in the evidence set.”
“What inconsistencies exist between witness W1 and W2?”
3. Legal research integration
Query case law by:
jurisdiction
court level
specific sections of civil codes
cited cases
4. Chronology builder
Auto-extract:
dates → events → actors → documents
Construct a case timeline.
5. Issue mapping
Use AI to classify arguments into legal issues:
breach of contract
causation
damages
procedural points
6. Argument generator
AI suggests:
defences
attack points
cross-examination questions
motions
settlement options
7. Hearing preparation
AI summarizes:
evidence bundles
witness contradictions
judge’s past decisions (if public)
🏗️ 3. Architecture for Litigation AI (Europe-friendly)
Here is a solution architecture specifically for litigation:
🧠 4. What AI Models You Should Use for Litigation
✔ LLM (GPT-style)
Reasoning
Summaries
Argument generation
Draft pleadings, letters
✔ Embedding Model
Similarity search across:
filings
evidence
emails
case law
✔ NLP Extraction Models
Named Entity Recognition (persons, companies, dates)
Event extraction (timeline creation)
Clause segmentation
Issue spotting
✔ OCR + Speech Models
Hearing transcripts
Scanned evidence
Audio calls
✔ CP-SAT (optional)
Lawyer calendars
Hearing scheduling
Evidence review workload
🇪🇺 5. European Historical Case Information: How AI Retrieves It
AI can retrieve historical litigation data by:
✔ Integrating with EU databases:
Curia (CJEU decisions)
EUR-Lex (all EU legislation and case law)
ECHR HUDOC (European Court of Human Rights decisions)
✔ National courts (country-specific APIs / scrapers)
Germany → juris / court websites
France → Legifrance
UK → National Archives (post-2022)
Netherlands → Rechtspraak.nl
Sweden → Sveriges Domstolar
✔ Commercial databases
LexisNexis
Westlaw
Beck-Online
Wolters Kluwer
✔ Firm’s own historical cases
Email archives
DMS
Past pleadings
Arbitration awards
Evidence bundles
Chronologies prepared by lawyers
AI transforms all this into a searchable knowledge graph for the case.
6. Litigation AI System Architecture
All AI layers work together:
LLM → drafting, reasoning, summarization
RAG → evidence retrieval, case law retrieval
NLP models → extracting facts, entities, timelines
OCR/Speech → converting physical evidence to text
CP-SAT → scheduling + workload optimization
External sources → EUR-Lex, Curia, national courts
Evidence DB → ingested filings, disclosures, emails
All under a single orchestrated architecture.
Legal NLP & Analytics Layer
Beyond retrieval, litigation requires deeper structure extraction from documents.
Entity Extraction
Identifies:
Parties
Judges
Courts
Dates
Locations
Citations
Contractual references
Built from multilingual models like:
XLM-RoBERTa
Legal-NER models
Domain-fine-tuned transformers
Issue Classification
Categorizes paragraphs into legal issues:
Liability
Breach
Causation
Damages
Jurisdiction challenges
Procedural defects
Timeline Extraction
Automates chronological reconstruction:
Events
Deadlines
Hearings
Filings
Precedent Classification
Links paragraphs to known legal concepts using embeddings.
This layer enables rich analytics and deep insight extraction from raw evidence.
🎯 6.1 . What We Will Build (End-to-End System)
6.1.1 Case Law Search & Indexing Layer
Litigation requires referencing both national and EU case law. Since the system is on-prem:
Local Indexing of Case Law Sources
EUR-Lex decisions
Curia (CJEU) judgments
HUDOC (ECHR) decisions
National court XML feeds (where allowed)
Indexed using:
Elasticsearch or Apache Solr
Optional embeddings for semantic case law search
Cross-Referencing Engine
Automatically links:
paragraphs → relevant case law
issues → corresponding precedent
citations → definitions/statutes
The platform becomes a private legal research engine tailored to the firm’s jurisdictions.
6.1.2. Optimization & Scheduling Layer (CP-SAT)
Litigation involves complex scheduling:
deadlines, court dates, evidence reviews, team workloads.
The platform uses Google OR-Tools CP-SAT to generate:
Lawyer workload balancing
Hearing calendars
Evidence review schedules
Deadline conflict alerts
Mediation/meeting slot optimization
Constraint programming ensures mathematically optimal allocation of resources.
6.1.3. Storage & Infrastructure Layer
The system’s foundation includes multiple storage components:
Relational DB (PostgreSQL)
Evidence metadata
User/matter mapping
Audit logs
NLP extraction results
Object Storage (MinIO)
PDFs
Exhibits
Audio files
OCR output
Vector DB (Qdrant/Milvus)
All embeddings for evidence
Case law embeddings
Timeline vectors
Full-Text Search (Elasticsearch/Solr)
Case law text
Non-semantic document search
Field-level queries
GPU/CPU Compute Nodes
LLM inference
OCR & STT batch processing
NLP model inference
This stack is deployable via:
Docker Compose (dev)
Kubernetes (K3s or full K8s) for production
6.1.4. Security, Compliance & Governance
Litigation requires strict controls. The system integrates:
Matter-based access control
Role-based permissions
Multi-tenant isolation
Audit logging for all prompts and outputs
Prompt redaction policies
Encrypted storage (SSE, LUKS)
TLS for all services
GDPR-compliant data flows
Air-gapped support for highly sensitive matters
Application-level authorization is enforced using Cerbos policies.
6.1.5. Deployment Model
The platform is optimized for private infrastructure:
On-Prem Kubernetes Cluster
API Gateway
LLM inference nodes
Vector DB
Elastic cluster
MinIO distributed storage
Evidence ingestion workers
Scheduling microservice
Celery worker pool
Scaling Model
Horizontal scaling of ingestion workers
Auto-scaling of inference nodes based on qps
Multi-node vector DB for large firms
Sharded elastic index for case law
Maintenance mode for evidence reindexing
🔧 6.2.Tech Stack (Fully On-Prem, Fully Open Source)
Backend
✔ Python 3.11
✔ FastAPI
✔ Celery for async ingestion
✔ Gunicorn/Uvicorn
Databases
✔ PostgreSQL
✔ MinIO for evidence storage
✔ Qdrant/Milvus vector DB
✔ Elasticsearch for case law & full-text
AI Models
✔ Mistral 7B / Mixtral / Llama3 (local)
✔ BGE-large / E5-large for embeddings
✔ Tesseract or PaddleOCR
✔ whisper.cpp
✔ HuggingFace NER + classifiers
Scheduling
✔ Google OR-Tools CP-SAT
Security
✔ Cerbos (already in your project!)
✔ JWT-based auth
✔ Matter-level access control
Deployment
✔ Docker Compose (dev)
✔ K3s Kubernetes cluster (prod)
✔ Optional GPU nodes for LLMs
✅ 6.3. PROJECT STRUCTURE — COMPLETE END-TO-END SCAFFOLDING
Your final project will look like this:
rag-system/
│
├── cerbos-config/
├── policies/
│
├── src/
│ ├── api/
│ │ ├── __init__.py
│ │ ├── routers/
│ │ │ ├── chat.py
│ │ │ ├── evidence.py
│ │ │ ├── caselaw.py
│ │ │ ├── scheduler.py
│ │ │ ├── admin.py
│ │ └── main.py
│ │
│ ├── core/
│ │ ├── config.py
│ │ ├── security.py
│ │ ├── logging_config.py
│ │ ├── errors.py
│ │ ├── utils.py
│ │
│ ├── db/
│ │ ├── postgres.py
│ │ ├── qdrant.py
│ │ ├── minio.py
│ │ ├── elastic.py
│ │ └── models/
│ │ ├── evidence.py
│ │ ├── caselaw.py
│ │ ├── metadata.py
│ │ └── scheduling.py
│ │
│ ├── services/
│ │ ├── llm/
│ │ │ ├── __init__.py
│ │ │ ├── llama_cpp_server.py
│ │ │ ├── prompts/
│ │ │ │ ├── chat_prompt.txt
│ │ │ │ ├── summary.txt
│ │ │ │ ├── legal_reasoning.txt
│ │ │ │ └── instructions.txt
│ │
│ │ ├── rag/
│ │ │ ├── retriever.py
│ │ │ ├── reranker.py
│ │ │ ├── chunking.py
│ │ │ ├── context_builder.py
│ │ │ └── pipeline.py
│ │
│ │ ├── ingestion/
│ │ │ ├── pipeline.py
│ │ │ ├── ocr.py
│ │ │ ├── speech_to_text.py
│ │ │ ├── email_parser.py
│ │ │ ├── metadata_extractor.py
│ │ │ ├── embedder.py
│ │ │ └── file_router.py
│ │
│ │ ├── nlp/
│ │ │ ├── ner.py
│ │ │ ├── issue_classifier.py
│ │ │ ├── timeline_extractor.py
│ │ │ ├── precedent_classifier.py
│ │ │ └── doc_classifier.py
│ │
│ │ ├── caselaw/
│ │ │ ├── indexer.py
│ │ │ ├── parser_eurlex.py
│ │ │ ├── parser_curia.py
│ │ │ ├── parser_hudoc.py
│ │ │ └── search.py
│ │
│ │ ├── scheduler/
│ │ │ ├── optimizer.py
│ │ │ ├── constraints.py
│ │ │ └── models.py
│ │
│ │ └── audits/
│ │ ├── audit_logger.py
│ │ └── guardrails.py
│ │
│ ├── workers/
│ │ ├── celery.py
│ │ └── tasks/
│ │ ├── ingest_task.py
│ │ ├── pdf_task.py
│ │ ├── embeddings_task.py
│ │ └── caselaw_index_task.py
│ │
│ ├── tests/
│ │ ├── test_api.py
│ │ ├── test_rag.py
│ │ ├── test_ingestion.py
│ │ ├── test_llm.py
│ │ └── test_scheduler.py
│ │
│ └── __init__.py
│
├── docker-compose.yml
├── Dockerfile
├── Makefile
├── requirements.txt
└── README.md
Conclusion
The blueprint above outlines a production-grade, defensible, secure, and fully open-source Litigation AI Platform engineered specifically for law firms, legal departments, and government agencies in Europe.
This architecture enables:
Evidence-centric retrieval and analysis
Local LLM reasoning without cloud dependency
Secure case law research
Automated drafting and summarization
Timeline reconstruction
Intelligent scheduling
Full compliance with data protection and legal practice rules
With this foundation, organizations can deliver AI-powered legal workflows while maintaining full control over sensitive litigation materials.
No comments:
Post a Comment