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Automating Legal Document Analysis & Contract Auditing

By Weavecode Team
Published 2026-07-14
2 min read
Automating Legal Document Analysis & Contract Auditing

Automating Legal Document Analysis & Contract Auditing

Reviewing commercial contracts (NDAs, SOWs, Master Services Agreements) is a bottleneck for corporate legal departments and sales operations. Senior legal reviewers spend hours verifying indemnification limits, payment terms, auto-renewal clauses, and liability caps.

Using Weavecode API and structured output schemas, developers can build automated contract auditing pipelines that flag high-risk terms in seconds.

1. Structured Contract Parsing Architecture

Legal documents require high-precision extraction. Standard text answers are insufficient; the audit pipeline must output typed schemas indicating risk categories, original snippets, and suggested revisions.

               ┌─────────────────┐
               │  Contract PDF   │
               └────────┬────────┘
                        ▼
               ┌─────────────────┐
               │   Docling/OCR   │
               └────────┬────────┘
                        ▼
               ┌─────────────────┐
               │  Weavecode API  │◄─────── [Pydantic Risk Schema]
               └────────┬────────┘
                        ▼
               ┌─────────────────┐
               │ Audit Dashboard │
               └─────────────────┘

2. Python Implementation Guide

We will define a Pydantic schema using Weavecode's endpoint to extract structured audit items.

Step 2.1: Define the Structured Output Schema

from pydantic import BaseModel, Field
from typing import List, Optional
 
class ContractClause(BaseModel):
    clause_type: str = Field(description="e.g. Liability Cap, Indemnification, Payment Term")
    snippet: str = Field(description="The exact text snippet extracted from the contract")
    risk_level: str = Field(description="Low, Medium, or High")
    summary: str = Field(description="A brief explanation of the clause requirements")
    reconciliation: Optional[str] = Field(description="Suggested edit or mitigation proposal")
 
class ContractAuditReport(BaseModel):
    contract_name: str
    effective_date: Optional[str]
    audit_findings: List[ContractClause]

Step 2.2: Invoke Weavecode's Extraction Endpoint

import os
from openai import OpenAI
 
client = OpenAI(base_url="https://api.weavecode.ai/v1", api_key=os.getenv("WEAVECODE_KEY"))
 
def audit_contract_text(contract_text: string) -> ContractAuditReport:
    response = client.beta.chat.completions.parse(
        model="anthropic/claude-3-5-sonnet",
        messages=[
            {"role": "system", "content": "You are a senior corporate counsel. Analyze the contract and extract compliance details."},
            {"role": "user", "content": contract_text}
        ],
        response_format=ContractAuditReport
    )
    return response.choices[0].message.parsed

3. Core Risk Audits

  • Liability Caps: Flagging contracts where liability exceeds 1x annual contract value (ACV) or is completely uncapped.
  • Auto-renewals: Extracting renewal notice periods (e.g., "Must notify 90 days before expiration") and pushing alerts to internal account managers.
  • Governing Law: Ensuring disputes are scoped strictly to familiar jurisdictions.

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