AI System for e-Discovery: Automating Legal Document Review
Imagine a lawsuit requiring analysis of 5 million documents in two weeks. Without AI, that would mean hundreds of lawyers working around the clock and budgets comparable to millions. We develop AI systems for e-Discovery that get the job done in days, cutting costs by 60–80%. Our team has 5+ years and 10+ projects, from startups to large law firms using AI for legal matters. The foundation is e-Discovery technology powered by machine learning.
How AI Accelerates e-Discovery?
Manual review of every document is a utopia. Modern models, such as fine-tuned BERT or LLMs with RAG, process terabytes of data and identify the relevant 1–5% in hours. Recall for relevant documents reaches 95%+, and for privileged documents — 99%. This isn't just time savings; it's a legal guarantee: missing a privileged document risks court sanctions. Electronic discovery becomes manageable with ML in jurisprudence.
Technologies We Use
The key component is Technology-Assisted Review (TAR), also known as Predictive Coding. We implement it via active learning with PyTorch or Hugging Face Transformers. The model trains on a seed set (thousands of documents labeled by lawyers) and then iteratively improves by selecting the most uncertain documents for labeling. This reduces manual effort by 10–20 times. As shown in the study Grossman & Cormack (2011), TAR cuts analysis time by 70-80% compared to linear review.
Example code: document classification
class DocumentRelevance(BaseModel): document_id: str relevance_score: float # 0-1 is_privileged: bool # attorney-client privilege is_responsive: bool # responsive to discovery request key_topics: list[str] custodians: list[str] # who is in the correspondence date: date | None def predict_relevance( document: str, seed_set: list[tuple[str, bool]] # (doc, is_relevant) for training ) -> DocumentRelevance: # Active Learning: pick most informative documents for labeling ... What is Technology-Assisted Review?
TAR is a method where a machine learning algorithm ranks documents by relevance. Attorneys review only top-ranked documents, and the model finetunes on their decisions. Vector search using a FAISS ANN index finds similar documents in milliseconds. Embedding models (OpenAI text-embedding-3-small or E5) generate 1536-dimensional vectors indexed in Qdrant or pgvector. This ensures high-speed processing of terabytes of data.
How We Detect Privileged Documents?
Attorney-client privilege covers documents exempt from disclosure. Missing such a document is a legal catastrophe. Our pipeline includes several layers:
- Domain filter: external counsel (e.g., @lawfirm.com)
- NLP model trained on phrases like "legal advice", "confidential", "attorney work product"
- Vector comparison with reference privileged documents
- Metadata-based validation (subject, participants, markings)
We target 99% recall for privileged documents, though this increases false positives which are filtered by attorneys. On average, 2–3% of the corpus is flagged as privileged.
Project Workflow
We handle the project turnkey. Stages:
- Analytics: audit data sources, EDRM modeling, define relevance and privilege criteria
- Integration: connectors to Exchange, SharePoint, Slack, Google Workspace, convert to unified format (RSMF) via Apache Tika
- Model Training: seed-set labeling, fine-tuning transformer models (BERT, RoBERTa), threshold tuning
- Validation: test on a holdout set, precision/recall metrics, legal sign-off
- Deployment: containerization (Docker), deployment on your servers or cloud (AWS, GCP), integration with Relativity or other platform
- Knowledge Transfer: documentation, team training, 3 months support
Comparison: TAR vs Linear Review
| Criterion | TAR (our approach) | Linear review (no AI) |
|---|---|---|
| Time to review 1M docs | 3 days | 50 days (100 lawyers) |
| Cost | Significantly lower | High |
| Recall relevant | 95% | 80% |
| Flexibility | Case-specific tuning | Static process |
| Privilege error rate | <1% | 5–10% |
Result: TAR is 10x faster and cheaper, while more accurate. We guarantee recall not lower than contractually agreed.
Embedding Model Comparison for e-Discovery
| Model | Dimension | Indexing speed (100k docs) | Recall@10 | Cost per 1k docs |
|---|---|---|---|---|
| OpenAI text-embedding-3-small | 1536 | 2 minutes | 95% | Low |
| E5-base | 768 | 3 minutes | 92% | Free |
| BERT-large | 1024 | 5 minutes | 90% | Requires GPU |
Embedding models are chosen for the task: OpenAI for high accuracy, open-source E5 for economy.
What's Included
- Model and API: a ready TAR model with REST API for uploading documents and getting predictions.
- Documentation: description of the pipeline, metrics, instructions for model updates.
- Access: login to a monitoring dashboard (W&B or MLflow) to see real-time metrics.
- Training: 2 days onsite or online for the legal team: how to label, interpret scores.
- Support: 3 months incident support, performance guarantee.
Typical Timeline
Cost is calculated individually, depending on data volume, number of custodians, and required speed. We estimate delivery from 2 to 6 weeks. A typical 2 million document project takes 3 weeks. We don't list fixed prices, but are ready to evaluate your case within 1 day. Contact us for an assessment.
Why Choose Us
We don't just deploy AI — we ensure the legal defensibility of the results. Our systems have passed court audits in the US and EU. 5+ years in the industry, 10+ projects, each with privileged recall > 99%. Turnkey work with metric guarantees. Schedule a consultation — let's discuss how to cut your e-Discovery costs.