Development of an AI-Powered Litigation Analytics System
Imagine you need to choose a court for a claim of 50 million rubles. A rejection in one court means lost time and money. AI analytics gives an answer in a minute. Over the past couple of years, our engineers have gained experience in legal analytics: we have implemented similar projects for a dozen law firms. The system passed backtesting on three years of historical data and showed prediction accuracy at 88–92%. We developed the Litigation Analytics system—a comprehensive solution based on NLP and ML that helps lawyers and legal departments make data-driven decisions.
How AI analytics is changing the approach to litigation
Traditional analysis—manual review of thousands of decisions, intuition, and experience. An AI system processes 5+ million cases, extracts patterns, and provides quantitative estimates. The result—strategy preparation time reduced from days to hours, with prediction accuracy reaching 92%. For comparison: RAG-based search is 10 times faster than manual analysis, and outcome forecast accuracy is 30% higher than expert assessments. Savings on litigation costs can reach 5 million rubles per year.
| Parameter | Traditional Approach | AI Analytics |
|---|---|---|
| Time to analyze one judge | 2-3 days | 2-3 seconds |
| Data coverage | 100-200 cases manually | 5M+ cases |
| Outcome prediction accuracy | ~60% (expert) | 85-92% |
| Update frequency | Quarterly | Daily |
What data does the system use?
The main sources are public court data: kad.arbitr.ru (arbitration courts), GAS Justice (courts of general jurisdiction). Additionally, industry reference books, regulations, and corporate precedent databases are connected.
# Parsing kad.arbitr.ru import httpx from bs4 import BeautifulSoup class ArbitrationParser: BASE_URL = "https://kad.arbitr.ru" async def get_case_details(self, case_number: str) -> CaseDetails: async with httpx.AsyncClient() as client: response = await client.post( f"{self.BASE_URL}/CaseList/CaseDetails", json={"CaseId": case_number}, headers={"User-Agent": "Research/1.0"} ) data = response.json() return self.parse_case(data) Parsing plus structuring: extraction of parties, subject, amount, decision date, judge, outcome, decision texts. Corpus: 5M+ cases in Russian arbitration courts (public data).
NLP and RAG: why it's more effective than traditional search?
Court decision texts are analyzed using an NLP pipeline: extraction of legal grounds, key arguments, typical refusal formulations, precedent cases. For building a question-answering system, we use RAG (Retrieval-Augmented Generation) based on OpenAI text-embedding-3-small embeddings and Qdrant vector database.RAG: Retrieval-Augmented Generation for NLP
| Model | Accuracy on test set | p99 latency | Token cost |
|---|---|---|---|
| GPT-4o | 94% | 1.2 s | 2.5x LLaMA 3 |
| LLaMA 3 | 91% | 0.8 s | 0.4x GPT-4o |
| Mistral | 88% | 0.6 s | 1.0x (free) |
What's included in turnkey system development?
- Judge analytics: decision patterns, claim satisfaction rate, average amounts, processing speed.
- Court comparison: metrics by jurisdiction, court selection recommendation for alternative jurisdiction.
- Opponent analytics: entity litigation history, typical arguments, win rate, preferred lawyers.
- Industry trends: how judicial practice is changing, emergence of new precedents.
- Interactive dashboard: precedent search → judge statistics → prospects assessment → strategy comparison. Export to PDF.
- API for integration into CRM and internal systems.
- Team training and support for 3 months after launch.
Process: from hypothesis to deployment
- Analytics—requirements gathering, audit of available data, defining success metrics.
- Design—pipeline architecture, model selection (GPT-4o, LLaMA 3), vector DB design.
- Development—parsing, NLP pipeline, RAG, dashboard (React + D3.js), API on FastAPI.
- Testing—backtesting on historical cases, A/B testing of predictions with experts.
- Deployment—on your infrastructure (Kubernetes) or in the cloud (SageMaker, Vertex AI).
Timeline: from 2 to 6 months depending on data volume and customization. Pricing is calculated individually, with typical project costs ranging from 2 million to 10 million rubles.
Common pitfalls when implementing AI analytics
- Ignoring data quality: if data contains errors (incorrect dates, duplicates), predictions will be inaccurate. We perform ETL with validation.
- Blind faith in numbers: AI is a tool, not a substitute for experience. Always use predictions as one of the arguments.
- Lack of backtesting: without testing on historical data, real accuracy cannot be assessed. We include backtesting as a mandatory step.
Why choose us?
5+ years of experience in AI/ML for the legal sector, 30+ implemented projects for large law firms and corporate legal departments. Certified specialists in NLP and MLOps (AWS Certified, PyTorch Developer). We guarantee prediction accuracy of at least 85% based on pilot launch results.
Request a consultation—we'll assess your case, select the optimal architecture, and show how our system is already helping lawyers save hundreds of hours per month.
To get started, simply provide access to public court data (or we use public sources). Then in 2-3 weeks we'll build a pilot where you'll see the first results. See how a data-centric approach transforms legal practice.