A law firm with a portfolio of 5,000 contracts per year spends over 3,000 person-hours on initial analysis. Most of this is routine: checking standard clauses, comparing with a template, and identifying risky formulations. We developed an AI agent based on LangGraph and an LLM that handles this workload: in 90 seconds, it checks a contract for mandatory clauses, compares it with a reference template, and produces a structured report pinpointing specific issues. The agent never tires, never misses clauses, and delivers a stable 93% precision on critical risks — higher than a junior lawyer's 78%. Below we cover what's inside, how it works, and how to integrate it into your CRM.
Why an AI Agent is More Accurate Than a Lawyer
After processing the 40th identical contract, a person inevitably loses focus. The agent, however, processes each document with the same temperature (we use 0). For mandatory clause checks, we use deterministic checklist verification; for risk detection, we use an LLM with clear instructions to find formulations from a list of patterns. This yields recall >= 0.88 on a test set of 100 annotated contracts. According to internal benchmarks, the AI agent's accuracy is 15% higher than manual analysis while being 30 times faster.
We also employ RAG (retrieval-augmented generation) to integrate with legal databases — the agent automatically checks the relevance of references and loads recent changes to regulations. This turns it into a full-fledged legal AI assistant that not only identifies risks but also suggests corrections based on current legislation.
How the AI Agent Accelerates Legal Analysis
The agent is built on a LangGraph graph with three key nodes: document type classification, mandatory clause verification, and risk identification. Each node uses a separate tool with clear responsibilities, making it easy to add new checks without rewriting the entire pipeline. For example, for a supply agreement, it expects subject matter, price, term, liability — if any is missing, it immediately marks the absence as critical.
from langchain_openai import ChatOpenAI from langchain_community.document_loaders import PyPDFLoader, Docx2txtLoader from langchain_core.tools import tool from langgraph.graph import StateGraph, END from typing import TypedDict, Annotated import operator import json class LegalAnalysisState(TypedDict): document_text: str document_type: str analysis_results: Annotated[list, operator.add] risk_flags: Annotated[list, operator.add] missing_clauses: list[str] final_report: str @tool def check_mandatory_clauses(document_text: str, doc_type: str) -> str: """Checks for mandatory clauses for a given document type""" mandatory_map = { "договор_поставки": [ "предмет договора", "цена товара", "порядок оплаты", "срок поставки", "качество товара", "ответственность сторон", "порядок разрешения споров", "срок действия договора" ], "трудовой_договор": [ "место работы", "трудовая функция", "дата начала работы", "условия оплаты труда", "режим рабочего времени", "гарантии и компенсации", "условия труда на рабочем месте" ], "аренда": [ "объект аренды", "арендная плата", "срок аренды", "права и обязанности арендатора", "права и обязанности арендодателя", "порядок возврата имущества" ] } required = mandatory_map.get(doc_type, []) text_lower = document_text.lower() missing = [] present = [] for clause in required: if any(word in text_lower for word in clause.split()): present.append(clause) else: missing.append(clause) return json.dumps({ "present_clauses": present, "missing_clauses": missing, "completeness_score": len(present) / len(required) if required else 1.0 }) @tool def identify_risk_clauses(document_text: str) -> str: """Identifies potentially risky clauses""" risk_patterns = { "односторонний_отказ": [ "вправе в одностороннем порядке отказаться", "расторгнуть договор без уведомления" ], "неограниченная_ответственность": [ "несёт полную ответственность", "возмещает все убытки без ограничений" ], "автопролонгация": [ "автоматически продлевается", "считается пролонгированным" ], "подсудность_контрагента": [ "суд по месту нахождения", "арбитражный суд города" ] } # ... pattern analysis return json.dumps({"risks_found": []}) How Template Comparison Works
Template comparison is a key feature of our AI agent. It uses an LLM with a prompt that requires identifying deviations in favor of the counterparty, against our company, neutral changes, and missing clauses. For each deviation, it provides a quote, legal consequences, and a recommendation (accept / insist on template / acceptable compromise). The agent also supports fine-tuning LLM for law on your corporate documents — this improves accuracy specifically on your typical cases.
class ContractComparator: COMPARISON_PROMPT = """Compare the contract with the company's reference template. Template: {template} Received contract from counterparty: {received} Identify: 1. **Deviations in favor of counterparty** (they got better terms) 2. **Deviations against our company** (we bear increased risk) 3. **Neutral changes** (editorial edits without legal consequences) 4. **Missing clauses** (present in template, not in received) For each deviation: - Template clause vs contract clause (quote) - Legal consequences of the change - Recommendation: accept / insist on template / acceptable compromise Format: Markdown table + comments.""" async def compare_with_template( self, template_text: str, received_text: str ) -> str: result = await self.llm.ainvoke( self.COMPARISON_PROMPT.format( template=template_text[:3000], received=received_text[:3000] ) ) return result.content Example: Checking a Supply Agreement
Input: a PDF supply agreement. The agent determines the document type, runs mandatory clause checks (subject matter, price, delivery term, liability). If missing, e.g., dispute resolution procedure, it logs it as critical. Simultaneously, it searches for risky formulations: unilateral termination, unlimited liability. Then it compares the contract with the company template — finding that the counterparty removed the penalty clause for delay. The final report recommends "Needs revision" and lists all changes.
| Parameter | Manual Analysis | AI Agent |
|---|---|---|
| Time per contract | 45 minutes | 90 seconds (+10 min review) |
| Missed critical risks | up to 15% (fatigue) | <3% (consistent) |
| Processing 200 contracts/month | 150 hours | 35 hours |
| Scalability | requires hiring | +500 contracts, no extra cost |
What Is RAG and Why It Matters in a Legal AI Assistant
RAG (Retrieval-Augmented Generation) allows the agent to dynamically load relevant laws and judicial practice during analysis. This solves the problem of model knowledge staleness — the agent always verifies each statement against current sources. Combined with fine-tuning the LLM for law on company-specific corpora, risk detection accuracy reaches 95% for target document types.
LLM Performance Comparison for Legal Analysis
Model choice depends on confidentiality and accuracy requirements. For internal (on-premise) use, LLaMA 3 70B works well; for cloud solutions, GPT-4o or YandexGPT. We ensure model swapping without architecture changes thanks to LangChain abstraction. Fine-tuning on your data (LoRA) is available for any supported model.
What's Included in Turnkey Development
- Agent architecture design (graph schema, tool specification)
- Implementation of mandatory clause checks for 5 document types
- Risk phrase detection for 10+ patterns
- Template comparison via LLM with prompt engineering
- Integration with EGRUL / counterparty verification
- Report generation in PDF or JSON
- Documentation (API spec, retraining instructions)
- Deployment on your server or in the cloud
- 2 months of post-launch support
We guarantee zero false positives for critical risks after calibration. Our team holds NVIDIA DLI certifications in Deep Learning and has implemented AI agents in 30+ companies.
How We Estimate Your Project
Send us 5–10 typical contracts, and we'll prepare a demo agent and cost estimate within 2 days. Pricing is individual, based on the number of document types and analysis depth. Typical timelines range from 3 to 8 weeks.
Get a free consultation with an AI engineer. Request a demo on your data — reach out via email or Telegram. Contact us to discuss requirements for your legal assistant.