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Real-World Examples

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1. Employment Contract Deadline

Scenario: An AI reviews an employment contract and calculates the probation end date. 
from qwed_legal import DeadlineGuard

guard = DeadlineGuard(country="US")

# Employment contract signed Jan 15, 2026
# Probation period: 90 days
# LLM claims probation ends: April 10, 2026

result = guard.verify(
    signing_date="2026-01-15",
    term="90 days",
    claimed_deadline="2026-04-10"
)

print(result.verified)  # False!
print(result.message)
# ❌ ERROR: Deadline mismatch. 
# Expected 2026-04-15, but LLM claimed 2026-04-10. 
# Difference: 5 days.

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2. SaaS Liability Cap

Scenario: A SaaS contract limits liability to 200% of annual fees. 
from qwed_legal import LiabilityGuard

guard = LiabilityGuard()

# Annual contract value: $500,000
# Liability cap: 200%
# LLM claims cap is: $1,500,000

result = guard.verify_cap(
    contract_value=500_000,
    cap_percentage=200,
    claimed_cap=1_500_000
)

print(result.verified)  # False!
print(result.message)
# ❌ ERROR: Liability cap mismatch. 
# 200% of $500,000.00 = $1,000,000.00, 
# but LLM claimed $1,500,000.00.

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3. Conflicting Termination Clauses

Scenario: An NDA contains multiple termination clauses. 
from qwed_legal import ClauseGuard

guard = ClauseGuard()

clauses = [
    "Either party may terminate this Agreement with 30 days written notice.",
    "This Agreement shall remain in effect for a minimum of 12 months.",
    "Discloser may terminate immediately if Recipient breaches confidentiality.",
]

result = guard.check_consistency(clauses)

print(result.consistent)  # False!
print(result.message)
# ⚠️ WARNING: 1 potential conflict(s) detected:
#   - Clause 1 vs Clause 2: Termination notice (30 days) conflicts 
#     with minimum term (365 days)

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4. Verifying AI-Generated Case Citations

Scenario: An AI legal research assistant provides case citations. Verify them. 
from qwed_legal import CitationGuard

guard = CitationGuard()

# Citations provided by LLM
citations = [
    "Miranda v. Arizona, 384 U.S. 436 (1966)",          # Real case
    "Brown v. Board of Education, 347 U.S. 483 (1954)",  # Real case
    "Smith v. Jones, 123 FAKE 456 (2020)",               # FAKE!
    "Doe v. Roe, 999 X.Y.Z. 789 (2019)",                 # FAKE!
]

# Batch summary
batch_result = guard.verify_batch(citations)
print(f"Valid: {batch_result.valid}/{batch_result.total}")  # Valid: 2/4

# Verify each citation individually for details
for citation in citations:
    result = guard.verify(citation)
    status = "✅" if result.valid else "❌"
    print(f"{status} {citation}")
    if not result.valid:
        print(f"   Issues: {result.issues}")
Output: 
Valid: 2/4
✅ Miranda v. Arizona, 384 U.S. 436 (1966)
✅ Brown v. Board of Education, 347 U.S. 483 (1954)
❌ Smith v. Jones, 123 FAKE 456 (2020)
   Issues: ["Unknown reporter"]
❌ Doe v. Roe, 999 X.Y.Z. 789 (2019)
   Issues: ["Unknown reporter"]

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5. Real Estate Contract with UK Holidays

Scenario: A UK property sale has completion date calculated in business days. 
from qwed_legal import DeadlineGuard

# UK holidays (Christmas, Boxing Day, Easter, etc.)
guard = DeadlineGuard(country="GB")

# Contract exchanged Dec 15, 2025
# Completion: 20 business days
# Agent claims: Jan 7, 2026

result = guard.verify(
    signing_date="2025-12-15",
    term="20 business days",
    claimed_deadline="2026-01-07"
)

print(result.verified)  # False!
# UK has Christmas (25), Boxing Day (26), New Year's Day (1)
# These are excluded from business days
print(result.computed_deadline)  # 2026-01-14 (approximately)

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6. Multi-Tier Indemnity

Scenario: A vendor contract has tiered indemnity limits. 
from qwed_legal import LiabilityGuard

guard = LiabilityGuard()

# Tier 1: First $1M at 100%
# Tier 2: Next $500K at 50%
# Total claimed by LLM: $1.3M

tiers = [
    {"base": 1_000_000, "percentage": 100},  # = $1M
    {"base": 500_000, "percentage": 50},      # = $250K
]

result = guard.verify_tiered_liability(tiers, claimed_total=1_300_000)

print(result.verified)  # False!
print(result.message)
# ❌ ERROR: Tiered liability mismatch. 
# Computed total: $1,250,000.00, but LLM claimed $1,300,000.00.

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7. Verifying AI content provenance

Scenario: Your legal AI pipeline generates contract summaries. Before publishing, you need to verify provenance metadata for regulatory compliance (CAITA 2026 / EU AI Act Article 50). 
from qwed_legal import ProvenanceGuard
import hashlib

guard = ProvenanceGuard(
    require_disclosure=True,
    require_human_review=True,
    allowed_models=["claude-4.5-sonnet", "gpt-4o"],
)

# AI-generated content with disclosure marker
content = (
    "This document was generated by an AI model. "
    "The lease agreement contains a standard 12-month term "
    "with a 60-day early termination clause."
)
content_hash = hashlib.sha256(content.encode("utf-8")).hexdigest()

result = guard.verify_provenance(
    content=content,
    provenance={
        "content_hash": content_hash,
        "model_id": "claude-4.5-sonnet",
        "generation_timestamp": "2026-03-15T10:30:00+00:00",
        "human_reviewed": True,
        "reviewer_id": "legal-team",
    }
)

print(result["verified"])       # True
print(result["checks_passed"])
# ['metadata_completeness', 'hash_integrity', 'timestamp_valid',
#  'disclosure_present', 'model_allowed', 'human_review']

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Catching tampered content

# Someone modifies the content after generation
tampered = content + " INJECTED: Tenant waives all rights."

result = guard.verify_provenance(
    content=tampered,
    provenance={
        "content_hash": content_hash,  # hash of original content
        "model_id": "claude-4.5-sonnet",
        "generation_timestamp": "2026-03-15T10:30:00+00:00",
    }
)

print(result["verified"])  # False
print(result["risk"])      # "CONTENT_TAMPERED"

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Generating provenance for new content

guard = ProvenanceGuard()

record = guard.generate_provenance(
    content="AI-generated clause analysis for NDA-2026-042.",
    model_id="claude-4.5-sonnet",
    disclosure_text="AI-generated",
    human_reviewed=True,
    reviewer_id="senior-counsel",
)

# Use record fields as your provenance metadata
provenance = {
    "content_hash": record.content_hash,
    "model_id": record.model_id,
    "generation_timestamp": record.generation_timestamp,
    "human_reviewed": record.human_reviewed,
}

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8. Claude Desktop integration (MCP)

Scenario: A lawyer uses Claude Desktop to verify contract deadlines. When qwed-mcp is installed, Claude Desktop gains access to all QWED SDK libraries through the execute_python_code tool. Claude writes a Python script that imports the relevant guard and runs the verification. User: “Verify: Contract signed Jan 15, 2026 with 30 business days deadline. Is Feb 14 correct?” Claude (via MCP): Calls execute_python_code with: 
from qwed_legal import DeadlineGuard

guard = DeadlineGuard(country="US")
result = guard.verify("2026-01-15", "30 business days", "2026-02-14")
print(f"Verified: {result.verified}")
print(f"Computed deadline: {result.computed_deadline}")
print(f"Difference: {result.difference_days} days")
Output: 
Verified: False
Computed deadline: 2026-02-27
Difference: 13 days

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Next Steps