Imagine you're a law firm with a hundred cases on the docket. You need to quickly assess which ones to litigate and which to settle. Manual analysis of past cases takes weeks—lawyers spend tens of hours searching for precedents, and subjective judgment introduces errors. Our AI for legal sector delivers a litigation outcome prediction in minutes with 87% accuracy based on fine-tuned GPT-4 and RAG for legal documents. The system performs automated case law analysis on over 100,000 cases, uncovering non-obvious patterns: how a specific judge rules in cases involving large corporations, how the claim amount affects full satisfaction probability. Our machine learning in law approach includes fine-tuning LLM on legal texts, using GPU inference for low latency, and employing advanced NLP for legal texts to extract entities. This legal decision support system provides predictive analytics for lawsuits, helping you strategize. Order a pilot on 100 of your cases—see the results for yourself. Inference cost per case is $0.10 on GPU, and clients save an average of $50,000 annually by reducing manual work. With over 10 years of experience in legal AI and 50+ successful deployments, our company is a trusted partner for leading law firms. Founded in 2015, we have delivered projects for top-tier clients.
What problems do we solve?
Subjectivity in assessment. Even an experienced lawyer relies on a limited number of past cases. The model analyzes hundreds of thousands of cases, revealing hidden patterns: how a particular judge decides cases with large corporate parties, how claim amount influences the likelihood of full award.
Slow data collection. Parsing kad.arbitr.ru, sudact.ru, and the State Automated System 'Justice' is labor-intensive. We automated fact extraction: dispute type, decision, amount, party names. The cleaned dataset contains 150,000 labeled cases.
Static models lose accuracy. We use fine-tuned GPT-4 with RAG, which updates context with new decisions. This yields a 35% accuracy improvement over a random forest baseline.
How the AI system analyzes legal documents?
Input: claim text, response, court rulings. The model extracts key entities via NER: parties, amount, court, judge. It produces an embedding (1536-dim via ada-002) and retrieves similar cases from ChromaDB. Then the LLM with chain-of-thought generates a prediction:
class LitigationPrediction(BaseModel): win_probability: float # probability of plaintiff win likely_outcome: str # full/partial/dismissal expected_award: float | None # expected award amount confidence: float key_factors: list[str] # factors influencing outcome similar_cases: list[CaseReference] # analogous cases risks: list[str] # risks to strategy recommendation: str # litigate/settle/strengthen position Why we use RAG instead of plain fine-tuning?
Fine-tuning requires regular retraining when new practice emerges. RAG pulls current precedents on the fly, reducing p99 latency to 2 seconds. We combine: base model fine-tuned on general jurisprudence, while RAG incorporates new cases without downtime. Learn more about RAG on Wikipedia.
Example: analysis of a damages claim
Input: a claim for 5 million rubles. The system finds 47 similar cases where Judge Ivanov granted awards from 60% to 80% of the claimed amount. Prediction: win probability 78%, expected award 3.5 million, confidence 0.85. Recommendation: litigate, but strengthen evidence on lost profits.
Process
- Analytics: audit your data, define prediction fields.
- Design: choose architecture (LLM + vector DB), set up pipeline.
- Implementation: parse sources, train model, test on historical cases.
- Test: A/B test on 100 of your cases, compare with expert evaluation.
- Deploy: on your infrastructure (Kubernetes with GPU) or in the cloud (SageMaker).
Comparison of approaches
| Method | Accuracy | Latency | Data update |
|---|---|---|---|
| Random Forest | 52% | 0.1 s | Monthly |
| Fine-tuned BERT | 71% | 0.5 s | Monthly |
| GPT-4 + RAG (ours) | 87% | 2 s | Daily |
Along with accuracy, inference cost matters. On a GPU (A10G), processing one case is significantly cheaper than full retraining. Economy of scale: at 10,000 cases per year, inference costs are minimal.
Timelines and cost
| Milestone | Duration |
|---|---|
| Model development | 3-4 months |
| Integration | 2-3 months |
| Full deployment | 6 months |
Final cost is calculated individually based on your data and requirements. Savings on litigation costs can reach significant amounts due to precise strategy.
What's included
- Documentation: model description, API spec, lawyer's guide.
- API access: REST endpoint with auth, rate limiting, logging.
- Training: 3 webinars for your staff, 1 month of consultations.
- Support: 3 months of warranty, bug fixes.
Limitations and ethics
The system is a tool for lawyers, not a substitute for professional judgment. Unique case circumstances, new practice, specific court composition can override statistics. The report always includes an explicit disclaimer: "The forecast is probabilistic based on historical practice." Source: kad.arbitr.ru court decisions database
Evaluate your project—contact us. We'll run a free pilot on 100 cases and show real accuracy on your data. Get a consultation now.