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How a Seller's Counsel Built Disclosure Schedules 10x Faster

Mage
Mage TeamLegal AI Experts
|
May 7, 2025·6 min read

Key Takeaways

  • Disclosure schedules completed in 3 days vs. typical 3 weeks
  • 147 material contracts categorized and mapped to schedules automatically
  • 8 disclosure gaps identified before buyer saw data room
  • Seller positioned for cleaner negotiation with complete disclosures

When a regional healthcare services company decided to sell, its counsel faced a familiar challenge: building comprehensive disclosure schedules from scratch while the buyer's diligence team was already asking for data room access.

The Challenge

The target company operated 12 outpatient clinics across three states. Over 15 years of operations, it had accumulated a complex web of contracts:

  • 89 payor agreements with commercial insurers and government programs
  • 23 real estate leases across the clinic locations
  • 18 physician employment agreements
  • 12 vendor contracts for medical equipment and supplies
  • 5 management services agreements

The PE buyer had provided a term sheet with aggressive timing: LOI signed Friday, data room access Monday, signed purchase agreement in 45 days.

Seller's counsel had 72 hours to prepare disclosure schedules that would withstand buyer diligence scrutiny. Traditional approach would take 3 weeks minimum.

The Approach

The deal team deployed Mage to accelerate disclosure schedule preparation.

Phase 1: Contract Ingestion (Day 1 Morning)

All 147 material contracts were uploaded to Mage. Within 4 hours, the platform had:

  • Classified each contract by type (payor, lease, employment, vendor, MSA)
  • Extracted key terms relevant to disclosure schedules
  • Identified which standard schedule sections each contract should populate

Phase 2: Schedule Mapping (Day 1 Afternoon)

Mage automatically mapped contract findings to standard APA schedule sections:

| Schedule Section | Contracts Mapped | |-----------------|------------------| | 3.12 Material Contracts | 147 | | 3.14 Intellectual Property | 3 | | 3.15 Insurance | 8 | | 3.16 Real Property | 23 | | 3.17 Employees | 18 | | 3.18 Employee Benefits | 6 |

Each mapping included the specific contract language supporting disclosure, allowing associates to verify accuracy instantly.

Phase 3: Gap Analysis (Day 2)

With contract extraction complete, the team focused on identifying disclosure gaps. Mage flagged 8 issues requiring additional disclosure:

  1. Payor termination rights: 3 payor contracts contained termination for convenience provisions not initially disclosed
  2. Lease renewal uncertainty: 2 leases had renewal options expiring within 6 months of expected closing
  3. Non-compete scope: 4 physician agreements contained non-compete provisions broader than typical
  4. Equipment liens: 1 equipment vendor retained a security interest in financed equipment
  5. Rate renegotiation: 2 payor contracts were subject to pending rate renegotiations
  6. Sublease restrictions: 1 lease prohibited assignment without landlord consent with specific conditions
  7. Key employee provisions: 3 employment agreements contained change of control provisions
  8. Insurance gaps: Current coverage did not match requirements in 2 vendor contracts

Phase 4: Schedule Drafting (Days 2-3)

With AI-extracted data verified, associates drafted disclosure language for each schedule section. Rather than reading contracts, they focused on:

  • Crafting appropriate disclosure language
  • Adding context and materiality qualifications
  • Ensuring consistency across related disclosures
  • Flagging items requiring seller input

The Results

Time Savings

  • Traditional timeline: 3 weeks for initial draft
  • With Mage: 3 days to complete schedules
  • Improvement: 10x faster delivery

Quality Improvement

  • 8 disclosure gaps caught before buyer review
  • Comprehensive extraction across all 147 contracts
  • Source-linked disclosures for easy verification

Strategic Advantage

  • Data room ready on Monday as promised
  • Buyer found well-organized, complete disclosures
  • Fewer diligence questions and smoother negotiation

The Outcome

When the buyer's diligence team reviewed the disclosure schedules, they found comprehensive, well-documented disclosures. The items Mage had flagged as gaps were properly disclosed, preventing potential post-closing disputes.

The purchase agreement negotiation proceeded smoothly. The buyer made few disclosure-related comments because the schedules were thorough. The deal closed on the original 45-day timeline.

Key Metrics

| Metric | Value | |--------|-------| | Material contracts analyzed | 147 | | Days to complete schedules | 3 | | Disclosure gaps identified | 8 | | Buyer disclosure comments | Minimal | | Deal timeline impact | On schedule |

Lessons Learned

Seller diligence matters. The time and cost to prepare thorough disclosures is far less than defending post-closing indemnification claims. Incomplete disclosure is expensive.

AI enables proactive disclosure. By analyzing all contracts systematically, Mage identified issues the seller's team might have missed. Better to disclose upfront than defend later.

Speed creates negotiating leverage. Delivering complete disclosures on day 1 of diligence positioned the seller as organized and transparent. Buyers respond positively to well-prepared sellers.

Source linking accelerates verification. Associates spent time drafting appropriate disclosure language rather than hunting through contracts. Every extraction linked directly to the source document.

Frequently Asked Questions

Why does seller's counsel care about disclosure schedules?

Complete, accurate disclosure schedules protect the seller from post-closing indemnification claims. Items properly disclosed are typically excluded from reps and warranties coverage. Gaps in disclosure can lead to substantial post-closing liability.

How did Mage speed up the process?

Mage automatically extracted key provisions from all 147 material contracts and mapped findings to standard disclosure schedule categories. Instead of manually reading each contract, associates verified AI extractions and focused on drafting appropriate disclosure language.

What happens if disclosure schedules are incomplete?

Incomplete disclosures create post-closing liability risk. If a buyer discovers an issue that should have been disclosed, they may have an indemnification claim. The cost of thorough upfront disclosure is far less than defending post-closing claims.

Can Mage help on the buy-side too?

Yes. Buy-side teams use Mage to verify seller disclosures against extracted contract data, identifying gaps the seller may have missed and informing indemnification negotiations.

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