What Is AI Redlining?
Redlining is the practice of marking up contracts to show proposed changes—insertions in one color, deletions in another. It's how lawyers have negotiated contracts for decades.
AI redlining automates this process: AI reads the contract, compares it against your standards, and produces markup showing suggested changes.
The key word is "markup." Not suggestions in a chat. Not a report listing issues. Actual tracked changes in the document.
How AI Redlining Works
Traditional Redlining
1. Receive contract from counterparty
2. Read every clause (30-60+ pages)
3. Compare each term to your standards
4. Mark changes in Word (Track Changes on)
5. Add comments explaining proposed changes
6. Send marked-up version back
Time: 2-8 hours depending on contract complexity.
AI Redlining
1. Receive contract from counterparty
2. Upload to AI redlining tool
3. AI reads document against your playbook
4. AI produces marked-up version with track changes
5. Attorney reviews AI suggestions (accept/reject)
6. Send marked-up version back
Time: 30-90 minutes including human review.
The difference: AI handles the mechanical comparison work. Humans handle judgment and strategy.
The Track Changes Requirement
Here's where most "AI contract review" tools fall short.
What Most Tools Produce
AI Report:
- Section 4.2: Indemnification is one-sided. Consider making mutual.
- Section 7.1: Liability cap is unlimited. Recommend capping at 2x fees.
- Section 9.3: Auto-renewal clause present. Consider removing.
Then you manually:
- Find Section 4.2 in Word
- Enable Track Changes
- Type your revisions
- Repeat for every issue
The AI found the issues. You did the redlining.
What True AI Redlining Produces
vendor_agreement_redlined.docx:
- Section 4.2: Text struck through, mutual language inserted (tracked)
- Section 7.1: "unlimited" deleted, "two times annual fees" inserted (tracked)
- Section 9.3: Auto-renewal clause marked for deletion (tracked)
- All changes attributed to "Contract Review AI" with timestamps
Open in Word: changes appear just like human redlines. Accept, reject, or modify each one.
The Technical Challenge
Word track changes aren't simple formatting. They're specific XML structures:
<!-- Deletion -->
<w:del w:id="1" w:author="Contract AI" w:date="2026-02-02T10:30:00Z">
<w:r>
<w:delText>unlimited liability</w:delText>
</w:r>
</w:del>
<!-- Insertion -->
<w:ins w:id="2" w:author="Contract AI" w:date="2026-02-02T10:30:00Z">
<w:r>
<w:t>liability capped at two times (2x) the annual fees paid</w:t>
</w:r>
</w:ins>
Most AI tools don't operate at this level. They generate text, not OOXML revisions.
AI Redlining Tools Compared
Legal-Specific Platforms
Spellbook
- Word-native integration
- Playbook customization
- Produces track changes
- Legal-focused training
- Premium pricing
Ironclad AI Assist
- Part of larger CLM platform
- Contract review suggestions
- Enterprise integration
- Requires Ironclad ecosystem
Kira Systems
- Due diligence focus
- Extraction + analysis
- Machine learning trained on legal docs
- Enterprise pricing
General Document AI
DocMods
- Direct DOCX manipulation
- Real track changes output
- API-first (programmable)
- Cross-industry use cases
- Developer-friendly
The Gap
Most "AI contract review" tools are analyzers, not editors. They read contracts and produce reports. The actual redlining is still manual.
True AI redlining requires:
- Reading the source document
- Understanding the OOXML structure
- Generating proper revision markup
- Preserving document formatting
- Maintaining author attribution
Building an AI Redlining Workflow
For Individual Contracts
from docxagent import DocxClient
def redline_contract(contract_path, playbook_rules):
client = DocxClient()
doc_id = client.upload(contract_path)
client.edit(
doc_id,
f"""Review this contract against our standards:
{playbook_rules}
For each deviation:
1. Mark the problematic text for deletion
2. Insert corrected language
3. Use track changes for all modifications
Focus on: payment terms, liability caps, indemnification,
IP ownership, termination rights, and governing law.""",
author="Contract Review AI"
)
output_path = contract_path.replace('.docx', '_redlined.docx')
client.download(doc_id, output_path)
return output_path
# Company playbook
playbook = """
- Payment: Net 30, no advance payments
- Liability: Capped at 12 months fees, mutual
- Indemnification: Mutual, limited to direct damages
- IP: We retain all pre-existing IP
- Termination: 30 days written notice, no penalty
- Governing law: Delaware
- Auto-renewal: Not acceptable, require explicit renewal
"""
redlined = redline_contract("vendor_msa.docx", playbook)
Output opens in Word with all AI changes tracked. Review, accept/reject, add comments, send.
For High-Volume Review
import os
from concurrent.futures import ThreadPoolExecutor
def batch_redline(folder_path, playbook):
client = DocxClient()
contracts = [f for f in os.listdir(folder_path) if f.endswith('.docx')]
def process_contract(filename):
filepath = os.path.join(folder_path, filename)
doc_id = client.upload(filepath)
client.edit(
doc_id,
f"Review against playbook and redline deviations:\n{playbook}",
author="Batch Contract AI"
)
output = os.path.join(folder_path, "redlined", filename)
client.download(doc_id, output)
return filename
os.makedirs(os.path.join(folder_path, "redlined"), exist_ok=True)
with ThreadPoolExecutor(max_workers=5) as executor:
results = list(executor.map(process_contract, contracts))
return results
# Process entire folder
processed = batch_redline("/contracts/incoming", playbook)
print(f"Redlined {len(processed)} contracts")
Due diligence scenarios: review 50 contracts in hours instead of weeks.
When AI Redlining Works Best
High-Value Use Cases
Vendor agreements (incoming)
- Consistent playbook application
- Catch unusual terms
- First-pass before attorney review
NDA review
- High volume, relatively standard
- Clear accept/reject criteria
- Quick turnaround needed
Lease review (real estate)
- Standard clause comparison
- Identify non-standard terms
- Flag missing protections
Employment agreements
- Compare to company standards
- Ensure compliance with policy
- Identify jurisdiction issues
When Human Review Is Still Critical
High-stakes negotiations
- Strategic considerations AI misses
- Relationship dynamics
- Novel business terms
Complex transactions
- M&A agreements
- Financing documents
- Regulatory filings
Unusual contract types
- Custom structures
- Industry-specific terms
- First-time relationships
The Human-AI Redlining Balance
The best workflows combine AI speed with human judgment:
Stage 1: AI First Pass
- AI redlines against playbook (5 minutes)
- Catches 80% of standard issues
- Produces tracked changes
Stage 2: Human Review
- Attorney reviews AI changes (20 minutes)
- Accepts clear improvements
- Rejects inappropriate suggestions
- Adds strategic changes
Stage 3: Human Polish
- Final read-through
- Add negotiation comments
- Ensure tone is appropriate
- Verify nothing missed
Total: 30 minutes vs 3 hours fully manual
AI doesn't replace attorneys. It makes them faster.
Evaluating AI Redlining Tools
Must-Have Features
- Produces actual track changes (not just reports)
- Preserves document formatting
- Customizable playbook/standards
- Clear author attribution on changes
- Works with your document workflow
Nice-to-Have Features
- Batch processing for due diligence
- Integration with contract management systems
- Learning from your accept/reject patterns
- Clause library for insertions
- Comparison across contract versions
Red Flags
- "AI-powered review" with no track changes output
- Requires copy-paste from chat interface
- Can't handle your formatting
- No customization for your standards
- Vague about how changes are made
The Bottom Line
AI redlining is powerful when it produces real tracked changes—not suggestions you implement manually.
The technology exists to automate contract markup while preserving the revision history legal and business workflows require. The key is choosing tools that operate at the document level, not just the text level.
Use AI for thoroughness and speed. Use humans for judgment and strategy. The combination beats either alone.
