Legal Tech AI Contract Analysis: 85% Faster Review with Safety Compliance

┌─────────────────────────────────────────────┐
│         Contract Documents Ingestion         │
│  • M&A agreements  • NDAs  • Employment     │
│  • Leases          • IP licenses            │
└────────────────┬────────────────────────────┘
                 │
                 ▼
┌─────────────────────────────────────────────┐
│      AI Contract Analysis Systems            │
│  ┌──────────────┐  ┌──────────────┐        │
│  │   Document   │  │   Clause     │        │
│  │   Analysis   │  │  Extraction  │        │
│  │  (Luminance, │  │  (LegalOn,   │        │
│  │  Spellbook)  │  │   LEGALFLY)  │        │
│  └──────┬───────┘  └──────┬───────┘        │
└─────────┼──────────────────┼────────────────┘
          │                  │
          ▼                  ▼
┌─────────────────────────────────────────────┐
│    🛡️ RAIL Score Legal Safety Layer 🛡️     │
│                                              │
│  ┌──────────────┐  ┌──────────────┐        │
│  │Hallucination │  │  Factual     │        │
│  │  Detection   │  │ Verification │        │
│  │              │  │ (vs contract)│        │
│  └──────────────┘  └──────────────┘        │
│                                              │
│  ┌──────────────┐  ┌──────────────┐        │
│  │   Context    │  │   Fairness   │        │
│  │Appropriateness│  │   Check      │        │
│  │ (Jurisdiction)│  │(Bias detect.)│        │
│  └──────────────┘  └──────────────┘        │
│                                              │
│  ┌──────────────┐  ┌──────────────┐        │
│  │ Completeness │  │ Confidence   │        │
│  │    Check     │  │ Calibration  │        │
│  └──────────────┘  └──────────────┘        │
└────────────┬───────────────────────────────┘
             │
             ▼
     [ Safety Decision Tree ]
             │
      ┌──────┴──────────┐
      ▼                 ▼
[ High Confidence ]  [ Require Senior Review ]
[ Junior Review OK ] [ Flag Specific Risks   ]

import os
from rail_score import RailScore
from typing import Dict, List, Any

# Initialize RAIL Score
rail_client = RailScore(api_key=os.environ.get("RAIL_API_KEY"))

class SafeLegalAISystem:
    """
    Law firm AI contract analysis with mandatory safety evaluation
    """

    def __init__(self, ai_contract_tool):
        self.ai_tool = ai_contract_tool  # Luminance, Spellbook, etc.
        self.rail = rail_client

        # Safety thresholds for legal work (higher than other industries)
        self.high_stakes_threshold = 95  # M&A, litigation
        self.standard_threshold = 90     # Standard contracts
        self.routine_threshold = 85      # NDAs, simple agreements

    def analyze_contract_with_safety(
        self,
        contract_document: bytes,
        contract_metadata: Dict,
        analysis_request: str
    ) -> Dict[str, Any]:
        """
        AI contract analysis with comprehensive safety evaluation
        """

        # Step 1: AI contract analysis
        ai_analysis = self.ai_tool.analyze(
            document=contract_document,
            request=analysis_request
        )

        # Step 2: Extract contract text for verification
        contract_text = self._extract_text(contract_document)

        # Step 3: Build legal context
        legal_context = self._build_legal_context(contract_metadata, analysis_request)

        # Step 4: RAIL Score safety evaluation
        rail_eval = self.rail.evaluate(
            prompt=legal_context["evaluation_prompt"],
            response=ai_analysis["summary"],
            categories=[
                "hallucination",        # Critical for legal accuracy
                "context_appropriateness",  # Jurisdiction-specific
                "fairness",             # Bias detection
                "completeness"          # Missed provisions
            ],
            metadata={
                "practice_area": contract_metadata["practice_area"],
                "jurisdiction": contract_metadata["jurisdiction"],
                "deal_value": "enterprise",
                "client_tier": contract_metadata["client_tier"]
            }
        )

        # Step 5: Factual verification against source document
        fact_check_result = self._verify_against_source(
            ai_claims=ai_analysis["key_findings"],
            source_text=contract_text
        )

        # Step 6: Completeness check
        completeness_check = self._check_analysis_completeness(
            ai_analysis=ai_analysis,
            contract_type=contract_metadata["contract_type"]
        )

        # Step 7: Jurisdiction appropriateness
        jurisdiction_check = self._check_jurisdiction_appropriateness(
            ai_analysis=ai_analysis,
            jurisdiction=contract_metadata["jurisdiction"]
        )

        # Step 8: Make safety decision
        safety_decision = self._make_legal_safety_decision(
            contract_metadata=contract_metadata,
            rail_eval=rail_eval,
            fact_check=fact_check_result,
            completeness=completeness_check,
            jurisdiction=jurisdiction_check
        )

        return {
            "ai_analysis": ai_analysis,
            "safety_evaluation": {
                "rail_score": rail_eval.overall_score,
                "rail_concerns": rail_eval.concerns,
                "fact_check": fact_check_result,
                "completeness": completeness_check,
                "jurisdiction_check": jurisdiction_check
            },
            "recommendation": safety_decision
        }

    def _build_legal_context(self, metadata: Dict, request: str) -> Dict:
        """
        Build context for RAIL Score evaluation
        """
        context_prompt = f"""
        Legal Contract Analysis Context:

        Contract Type: [CONTRACT_TYPE]
        Practice Area: [PRACTICE_AREA]
        Jurisdiction: [JURISDICTION]
        Deal Value: [DEAL_VALUE]
        Client: [CLIENT_TIER] tier

        Analysis Request:
        [REQUEST]

        This analysis will be used for client advice in a legal matter.
        Accuracy and completeness are critical. Hallucinations or
        omissions could constitute legal malpractice.
        """

        return {
            "evaluation_prompt": context_prompt,
            "risk_level": self._determine_risk_level(metadata)
        }

    def _verify_against_source(self, ai_claims: List[Dict], source_text: str) -> Dict:
        """
        Verify every factual claim made by AI against source contract
        """
        verification_results = []
        errors_found = []

        for claim in ai_claims:
            if claim["type"] == "factual":
                # Check if claim is supported by source text
                is_verified = self._find_evidence_in_source(
                    claim["statement"],
                    source_text
                )

                verification_results.append({
                    "claim": claim["statement"],
                    "verified": is_verified,
                    "severity": claim.get("importance", "medium")
                })

                if not is_verified:
                    errors_found.append({
                        "claim": claim["statement"],
                        "error_type": "unsupported_claim",
                        "severity": claim.get("importance", "medium")
                    })

        # Check for numerical accuracy (dates, dollar amounts, percentages)
        numerical_accuracy = self._verify_numerical_claims(ai_claims, source_text)

        return {
            "verified": len(errors_found) == 0 and numerical_accuracy["accurate"],
            "verification_results": verification_results,
            "errors": errors_found + numerical_accuracy.get("errors", []),
            "confidence": calculate_verification_confidence(verification_results)
        }

    def _check_analysis_completeness(self, ai_analysis: Dict, contract_type: str) -> Dict:
        """
        Check if AI analysis covers all critical provisions for contract type
        """

        # Define required provisions by contract type
        required_provisions = {
            "M&A_agreement": [
                "purchase_price",
                "closing_conditions",
                "representations_and_warranties",
                "indemnification",
                "termination_rights",
                "non_compete",
                "dispute_resolution"
            ],
            "employment_agreement": [
                "compensation",
                "benefits",
                "termination_provisions",
                "non_compete",
                "confidentiality",
                "dispute_resolution"
            ],
            "NDA": [
                "definition_of_confidential_info",
                "permitted_disclosures",
                "duration",
                "return_of_materials",
                "remedies"
            ]
        }

        required = required_provisions.get(contract_type, [])
        addressed = ai_analysis.get("provisions_analyzed", [])

        missing = [prov for prov in required if prov not in addressed]

        return {
            "complete": len(missing) == 0,
            "missing_provisions": missing,
            "completeness_score": len(addressed) / len(required) if required else 1.0
        }

    def _check_jurisdiction_appropriateness(self, ai_analysis: Dict, jurisdiction: str) -> Dict:
        """
        Verify AI analysis accounts for jurisdiction-specific law
        """
        issues = []

        # Check for jurisdiction-specific concerns
        if jurisdiction == "California":
            # California treats non-competes differently
            if "non_compete" in ai_analysis.get("provisions_analyzed", []):
                if not self._mentions_california_restrictions(ai_analysis):
                    issues.append("AI analysis does not address California's prohibition on non-competes")

        if jurisdiction == "New York":
            # New York has specific employment law requirements
            if ai_analysis.get("contract_type") == "employment_agreement":
                if not self._addresses_ny_labor_law(ai_analysis):
                    issues.append("AI analysis may not fully address NY Labor Law requirements")

        # Check if AI made jurisdiction-inappropriate recommendations
        inappropriate = self._detect_jurisdiction_conflicts(ai_analysis, jurisdiction)

        return {
            "appropriate": len(issues) == 0 and len(inappropriate) == 0,
            "concerns": issues + inappropriate,
            "requires_local_counsel_review": len(issues) > 0
        }

    def _make_legal_safety_decision(
        self,
        contract_metadata,
        rail_eval,
        fact_check,
        completeness,
        jurisdiction
    ) -> Dict:
        """
        Determine appropriate review level based on safety evaluation
        """

        # Determine risk-appropriate threshold
        if contract_metadata.get("deal_value", 0) > 100_000_000:
            threshold = self.high_stakes_threshold  # 95
        elif contract_metadata["practice_area"] in ["M&A", "litigation"]:
            threshold = self.high_stakes_threshold  # 95
        elif contract_metadata["client_tier"] == "tier_1":
            threshold = self.standard_threshold  # 90
        else:
            threshold = self.routine_threshold  # 85

        # Critical failures - block and require senior partner review
        if not fact_check["verified"]:
            return {
                "decision": "block_ai_output",
                "reason": "Factual verification failed - AI made unsupported claims",
                "errors": fact_check["errors"],
                "required_action": "Senior partner review required",
                "use_ai_output": False,
                "priority": "critical"
            }

        if rail_eval.hallucination_risk == "high":
            return {
                "decision": "block_ai_output",
                "reason": "High hallucination risk detected",
                "required_action": "Senior partner review, do not use AI analysis",
                "use_ai_output": False,
                "priority": "critical"
            }

        if rail_eval.overall_score < threshold - 15:  # Well below threshold
            return {
                "decision": "require_senior_review",
                "reason": f"Safety score {rail_eval.overall_score} below threshold {threshold}",
                "concerns": rail_eval.concerns,
                "required_action": "Senior partner must review before client delivery",
                "use_ai_as_draft": True,
                "priority": "high"
            }

        # Moderate concerns
        if threshold - 15 <= rail_eval.overall_score < threshold:
            return {
                "decision": "require_experienced_associate_review",
                "reason": f"Safety score {rail_eval.overall_score} near threshold {threshold}",
                "concerns": rail_eval.concerns,
                "required_action": "Experienced associate review with specific attention to flagged areas",
                "review_focus_areas": rail_eval.concerns,
                "use_ai_as_draft": True
            }

        # Completeness issues
        if not completeness["complete"]:
            return {
                "decision": "flag_for_supplemental_analysis",
                "reason": "AI analysis incomplete",
                "missing_provisions": completeness["missing_provisions"],
                "required_action": "Associate must analyze missing provisions before client delivery",
                "use_ai_as_starting_point": True
            }

        # Jurisdiction concerns
        if not jurisdiction["appropriate"]:
            return {
                "decision": "require_local_counsel_review",
                "reason": "Jurisdiction-specific concerns detected",
                "concerns": jurisdiction["concerns"],
                "required_action": "Consult local counsel or experienced partner in jurisdiction",
                "use_ai_with_caution": True
            }

        # High safety score - can be used with standard associate review
        if rail_eval.overall_score >= threshold:
            return {
                "decision": "approve_with_standard_review",
                "reason": f"Safety score {rail_eval.overall_score} meets threshold {threshold}",
                "required_action": "Standard associate review before client delivery",
                "confidence": "high",
                "use_ai_output": True,
                "ai_quality": "high"
            }

# Example usage
legal_ai = SafeLegalAISystem(ai_contract_tool=SpellbookAPI())

contract_result = legal_ai.analyze_contract_with_safety(
    contract_document=load_pdf("merger_agreement.pdf"),
    contract_metadata={
        "contract_type": "M&A_agreement",
        "practice_area": "M&A",
        "jurisdiction": "Delaware",
        "deal_value": 200_000_000,
        "client_tier": "tier_1"
    },
    analysis_request="Summarize key terms, identify risks, and highlight unusual provisions"
)

print(contract_result["recommendation"])
# Output: {'decision': 'approve_with_standard_review', 'confidence': 'high', ...}

rail_eval = rail_client.evaluate(
    prompt="Analyze non-compete clause in M&A agreement, jurisdiction: California and Delaware",
    response="Non-compete clause: Standard 2-year restriction, enforceable in all jurisdictions.",
    categories=["hallucination", "context_appropriateness"]
)

# Result:
# - Overall Score: 34/100 🚨
# - Hallucination Risk: HIGH
# - Context Appropriateness: 28/100
# - Concerns: [
#     "Claim of 'standard 2-year' not verifiable from source",
#     "Jurisdictional analysis overly broad - California has specific restrictions",
#     "No mention of enforceability nuances by jurisdiction"
#   ]

fact_check = verify_against_source(
    ai_claim="2-year restriction",
    source_contract=merger_agreement_text
)

# Result:
# - Verified: FALSE
# - Actual contract text: "Seller agrees not to compete...for a period of five (5) years"
# - Error: AI hallucinated duration (said 2 years, contract says 5 years)

🚨 BLOCK AI OUTPUT - CRITICAL SAFETY FAILURE

Reason: Factual verification failed
- AI claimed 2-year non-compete
- Actual contract states 5-year non-compete
- This is a material misstatement

Additionally:
- RAIL Score: 34/100 (well below threshold of 95 for M&A)
- High hallucination risk detected
- Context appropriateness concerns for California jurisdiction

REQUIRED ACTION: Senior partner review mandatory.
DO NOT use AI analysis. Perform manual contract review.

Priority: CRITICAL

ai_review_standards = {
    "routine_contracts": "Experienced associate review",
    "complex_contracts": "Senior associate or junior partner review",
    "high_stakes_matters": "Senior partner review mandatory",
    "novel_legal_issues": "Do not use AI, human analysis only"
}

def mandatory_fact_check(ai_output, source_documents):
    """
    Verify all dates, amounts, names, provisions cited by AI
    """
    factual_claims = extract_factual_claims(ai_output)

    for claim in factual_claims:
        evidence = find_evidence_in_source(claim, source_documents)

        if not evidence:
            raise AIHallucinationError(
                f"AI claim '{claim}' not found in source documents. "
                "Block AI output and require human review."
            )

safety_thresholds_by_practice = {
    "M&A": 95,              # Highest stakes
    "litigation": 95,        # High risk
    "employment_law": 92,    # Regulatory complexity
    "real_estate": 90,       # Standard complexity
    "NDAs": 85,             # Lower complexity
    "routine_contracts": 85  # Standardized work
}

def validate_jurisdiction_appropriateness(ai_analysis, jurisdiction):
    """
    Ensure AI analysis accounts for jurisdiction-specific law
    """
    if jurisdiction in ["California", "New York", "Texas"]:
        # These jurisdictions have many state-specific laws
        require_local_counsel_review = True

    if ai_analysis.mentions_enforceability:
        # Always verify enforceability claims are jurisdiction-specific
        verify_jurisdiction_specific_analysis(ai_analysis, jurisdiction)

associate_training_program = {
    "manual_contract_review": "20% of work remains fully manual",
    "ai_output_critique": "Associates must identify AI errors in training exercises",
    "continuing_legal_education": "CLE on AI limitations and legal ethics",
    "peer_review": "Partners spot-check associate reviews of AI outputs"
}

def create_ai_audit_trail(matter_id, ai_usage):
    """
    Document AI usage for ethics and malpractice defense
    """
    audit_record = {
        "matter_id": matter_id,
        "ai_tool_used": ai_usage["tool_name"],
        "ai_output": ai_usage["output"],
        "rail_safety_score": ai_usage["rail_score"],
        "human_reviewer": ai_usage["reviewing_attorney"],
        "review_level": ai_usage["review_thoroughness"],
        "client_notified_of_ai_use": ai_usage["client_consent"],
        "timestamp": datetime.now()
    }

    store_in_matter_file(audit_record)