Contract Clause Analyzer

Business Case

Problem Statement

Contract review is time-consuming and error-prone:

• Important clauses missed
• Risk provisions overlooked
• Inconsistent interpretation
• Long review cycles

Solution

AI-assisted contract clause analysis that identifies key provisions, flags risks, and extracts critical terms.

Technical Implementation

import pandas as pd
from datetime import datetime, date
from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from enum import Enum
import re


class ClauseType(Enum):
    SCOPE = "scope"
    PAYMENT = "payment"
    SCHEDULE = "schedule"
    CHANGE_ORDER = "change_order"
    TERMINATION = "termination"
    INDEMNIFICATION = "indemnification"
    INSURANCE = "insurance"
    WARRANTY = "warranty"
    DISPUTE = "dispute"
    LIABILITY = "liability"
    FORCE_MAJEURE = "force_majeure"
    SAFETY = "safety"
    COMPLIANCE = "compliance"
    OTHER = "other"


class RiskLevel(Enum):
    HIGH = "high"
    MEDIUM = "medium"
    LOW = "low"
    INFO = "info"


@dataclass
class ContractClause:
    clause_id: str
    section: str
    title: str
    text: str
    clause_type: ClauseType
    risk_level: RiskLevel
    key_terms: List[str] = field(default_factory=list)
    obligations: List[str] = field(default_factory=list)
    deadlines: List[str] = field(default_factory=list)
    amounts: List[str] = field(default_factory=list)
    notes: str = ""


@dataclass
class AnalysisResult:
    contract_name: str
    analyzed_date: datetime
    total_clauses: int
    clauses: List[ContractClause]
    risk_summary: Dict[str, int]
    key_dates: List[Dict[str, str]]
    key_amounts: List[Dict[str, str]]


class ContractClauseAnalyzer:
    """Analyze construction contract clauses."""

    RISK_KEYWORDS = {
        'high': ['indemnify', 'sole discretion', 'waive', 'forfeit', 'liquidated damages',
                 'consequential', 'unlimited liability', 'hold harmless', 'no limit'],
        'medium': ['shall', 'must', 'required', 'obligated', 'responsible', 'liable',
                   'penalty', 'default', 'breach'],
        'low': ['may', 'should', 'reasonable', 'mutual', 'consent', 'approval']
    }

    CLAUSE_PATTERNS = {
        ClauseType.PAYMENT: ['payment', 'invoice', 'retainage', 'progress payment'],
        ClauseType.SCHEDULE: ['schedule', 'completion date', 'milestone', 'time is of the essence'],
        ClauseType.CHANGE_ORDER: ['change order', 'modification', 'additional work', 'variation'],
        ClauseType.TERMINATION: ['termination', 'terminate', 'cancellation'],
        ClauseType.INDEMNIFICATION: ['indemnif', 'hold harmless', 'defend'],
        ClauseType.INSURANCE: ['insurance', 'coverage', 'policy', 'insured'],
        ClauseType.WARRANTY: ['warranty', 'guarantee', 'defect', 'workmanship'],
        ClauseType.DISPUTE: ['dispute', 'arbitration', 'mediation', 'litigation'],
        ClauseType.LIABILITY: ['liability', 'damages', 'limitation'],
        ClauseType.FORCE_MAJEURE: ['force majeure', 'act of god', 'unforeseen'],
    }

    def __init__(self):
        self.clauses: List[ContractClause] = []

    def analyze_text(self, contract_name: str, text: str) -> AnalysisResult:
        """Analyze contract text."""
        self.clauses = []

        # Split into sections/clauses
        sections = self._split_into_sections(text)

        for i, section in enumerate(sections):
            clause = self._analyze_clause(f"CL-{i+1:03d}", section)
            self.clauses.append(clause)

        # Generate summary
        risk_summary = {
            'high': sum(1 for c in self.clauses if c.risk_level == RiskLevel.HIGH),
            'medium': sum(1 for c in self.clauses if c.risk_level == RiskLevel.MEDIUM),
            'low': sum(1 for c in self.clauses if c.risk_level == RiskLevel.LOW)
        }

        key_dates = []
        key_amounts = []
        for clause in self.clauses:
            for d in clause.deadlines:
                key_dates.append({'clause': clause.clause_id, 'date': d})
            for a in clause.amounts:
                key_amounts.append({'clause': clause.clause_id, 'amount': a})

        return AnalysisResult(
            contract_name=contract_name,
            analyzed_date=datetime.now(),
            total_clauses=len(self.clauses),
            clauses=self.clauses,
            risk_summary=risk_summary,
            key_dates=key_dates,
            key_amounts=key_amounts
        )

    def _split_into_sections(self, text: str) -> List[Dict[str, str]]:
        """Split contract into sections."""
        sections = []
        # Simple split by numbered sections
        pattern = r'(\d+\.[\d\.]*\s+[A-Z][^\.]+)'
        parts = re.split(pattern, text)

        current_title = ""
        for i, part in enumerate(parts):
            if re.match(r'\d+\.[\d\.]*\s+[A-Z]', part):
                current_title = part.strip()
            elif part.strip() and current_title:
                sections.append({
                    'title': current_title,
                    'text': part.strip()
                })
                current_title = ""

        # If no sections found, treat whole text as one
        if not sections and text.strip():
            sections.append({'title': 'Contract Text', 'text': text.strip()})

        return sections

    def _analyze_clause(self, clause_id: str, section: Dict[str, str]) -> ContractClause:
        """Analyze single clause."""
        text = section.get('text', '')
        title = section.get('title', '')
        text_lower = text.lower()

        # Determine clause type
        clause_type = self._determine_type(text_lower)

        # Assess risk level
        risk_level = self._assess_risk(text_lower)

        # Extract key terms
        key_terms = self._extract_key_terms(text)

        # Extract obligations
        obligations = self._extract_obligations(text)

        # Extract dates
        deadlines = self._extract_dates(text)

        # Extract amounts
        amounts = self._extract_amounts(text)

        return ContractClause(
            clause_id=clause_id,
            section=clause_id,
            title=title,
            text=text[:500] + "..." if len(text) > 500 else text,
            clause_type=clause_type,
            risk_level=risk_level,
            key_terms=key_terms,
            obligations=obligations,
            deadlines=deadlines,
            amounts=amounts
        )

    def _determine_type(self, text: str) -> ClauseType:
        """Determine clause type from content."""
        for clause_type, keywords in self.CLAUSE_PATTERNS.items():
            if any(kw in text for kw in keywords):
                return clause_type
        return ClauseType.OTHER

    def _assess_risk(self, text: str) -> RiskLevel:
        """Assess risk level of clause."""
        high_count = sum(1 for kw in self.RISK_KEYWORDS['high'] if kw in text)
        medium_count = sum(1 for kw in self.RISK_KEYWORDS['medium'] if kw in text)

        if high_count >= 2:
            return RiskLevel.HIGH
        elif high_count >= 1 or medium_count >= 3:
            return RiskLevel.MEDIUM
        elif medium_count >= 1:
            return RiskLevel.LOW
        return RiskLevel.INFO

    def _extract_key_terms(self, text: str) -> List[str]:
        """Extract key defined terms."""
        # Look for quoted terms or capitalized multi-word phrases
        patterns = [
            r'"([^"]+)"',
            r"'([^']+)'",
            r'\b([A-Z][a-z]+(?:\s+[A-Z][a-z]+)+)\b'
        ]
        terms = []
        for pattern in patterns:
            matches = re.findall(pattern, text)
            terms.extend(matches[:5])
        return list(set(terms))[:10]

    def _extract_obligations(self, text: str) -> List[str]:
        """Extract obligation statements."""
        patterns = [
            r'(?:contractor|owner|party)\s+shall\s+([^\.]+)',
            r'(?:contractor|owner|party)\s+must\s+([^\.]+)',
            r'(?:contractor|owner|party)\s+is\s+(?:required|obligated)\s+to\s+([^\.]+)'
        ]
        obligations = []
        for pattern in patterns:
            matches = re.findall(pattern, text, re.IGNORECASE)
            obligations.extend(matches[:3])
        return obligations[:5]

    def _extract_dates(self, text: str) -> List[str]:
        """Extract date references."""
        patterns = [
            r'\b\d{1,2}/\d{1,2}/\d{2,4}\b',
            r'\b(?:January|February|March|April|May|June|July|August|September|October|November|December)\s+\d{1,2},?\s+\d{4}\b',
            r'\b\d+\s+(?:calendar|working|business)\s+days\b',
            r'\bwithin\s+\d+\s+days\b'
        ]
        dates = []
        for pattern in patterns:
            matches = re.findall(pattern, text, re.IGNORECASE)
            dates.extend(matches)
        return dates[:5]

    def _extract_amounts(self, text: str) -> List[str]:
        """Extract monetary amounts."""
        patterns = [
            r'\$[\d,]+(?:\.\d{2})?',
            r'\b\d+(?:,\d{3})*(?:\.\d{2})?\s*(?:dollars|USD)\b',
            r'\b\d+(?:\.\d+)?%\b'
        ]
        amounts = []
        for pattern in patterns:
            matches = re.findall(pattern, text, re.IGNORECASE)
            amounts.extend(matches)
        return amounts[:5]

    def get_high_risk_clauses(self) -> List[ContractClause]:
        """Get all high-risk clauses."""
        return [c for c in self.clauses if c.risk_level == RiskLevel.HIGH]

    def export_analysis(self, result: AnalysisResult, output_path: str):
        """Export analysis to Excel."""
        with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
            # Summary
            summary_df = pd.DataFrame([{
                'Contract': result.contract_name,
                'Analyzed': result.analyzed_date,
                'Total Clauses': result.total_clauses,
                'High Risk': result.risk_summary['high'],
                'Medium Risk': result.risk_summary['medium'],
                'Low Risk': result.risk_summary['low']
            }])
            summary_df.to_excel(writer, sheet_name='Summary', index=False)

            # Clauses
            clause_data = [{
                'ID': c.clause_id,
                'Title': c.title[:50],
                'Type': c.clause_type.value,
                'Risk': c.risk_level.value,
                'Key Terms': ', '.join(c.key_terms[:3]),
                'Obligations': len(c.obligations),
                'Dates': ', '.join(c.deadlines[:2]),
                'Amounts': ', '.join(c.amounts[:2])
            } for c in result.clauses]
            pd.DataFrame(clause_data).to_excel(writer, sheet_name='Clauses', index=False)

        return output_path