How a Corporate Team Managed 15 Simultaneous Acquisitions

A PE-backed home services platform had an aggressive growth mandate: acquire 15 regional competitors within 12 months to achieve national scale. The challenge was executing consistent, thorough diligence across all transactions with a 3-person corporate development team.

The Challenge

The platform company provided residential HVAC, plumbing, and electrical services. The PE sponsor's investment thesis depended on building national scale through acquisition of regional operators, then applying operational best practices to improve margins across the portfolio.

The acquisition targets were similar: family-owned or small PE-backed operators with $10-30M in revenue, 50-150 employees, and established customer bases in their markets.

The corporate development team consisted of:

• VP of Corporate Development (deal sourcing, negotiation, integration)
• Senior M&A Counsel (legal diligence, contract negotiation)
• M&A Analyst (financial diligence, data room management)

Traditional staffing would require 2-3 outside counsel associates per active deal. At peak velocity with 8 simultaneous active transactions, that meant 16-24 outside lawyers plus the internal team—a coordination nightmare and significant expense.

The Approach

The team deployed Mage to transform their diligence capacity without proportionally scaling headcount.

Standardized Diligence Framework

The team established a standard extraction framework for all acquisitions:

| Category | Key Provisions Extracted | |----------|-------------------------| | Customer Contracts | Term, renewal, termination rights, pricing, assignment | | Vendor Agreements | Exclusivity, liability caps, insurance requirements | | Real Estate | Lease terms, rent escalation, assignment restrictions | | Employment | Compensation, benefits, non-competes, change of control | | Equipment | Financing terms, liens, maintenance obligations |

Every target received the same comprehensive analysis, regardless of deal velocity or team bandwidth.

Workflow Optimization

The team restructured their deal process around AI-powered diligence:

Day 1-2: Data Room Ingestion All data room documents uploaded to Mage immediately upon access. AI classification and extraction began automatically.

Day 3-5: Issue Identification The Senior M&A Counsel reviewed AI-flagged issues rather than reading documents. High-priority issues escalated to deal team discussions.

Day 6-30: Issue Resolution Attorney time focused on negotiating solutions to identified issues rather than finding issues. Outside counsel engaged only for specialized matters requiring expertise beyond the standard diligence scope.

Day 31-Close: Integration Preparation With diligence substantially complete, the team shifted to integration planning while closing conditions were satisfied.

Managing Multiple Simultaneous Deals

At peak activity, the team had 8 transactions in active diligence simultaneously at various stages:

| Deal | Stage | Documents | Key Issues | |------|-------|-----------|------------| | Target A | Week 1 | 340 | Classification in progress | | Target B | Week 2 | 520 | 3 customer COC issues | | Target C | Week 3 | 180 | Clean, moving to PA negotiation | | Target D | Week 2 | 290 | 2 lease assignment issues | | Target E | Week 4 | 410 | Employee retention concerns | | Target F | Week 1 | 260 | Classification in progress | | Target G | Week 3 | 380 | Equipment lien issues | | Target H | Week 5 | 310 | Closing checklist items |

The Mage dashboard provided visibility across all deals, enabling the team to allocate attention based on actual issues rather than arbitrary deal stage.

Cross-Deal Pattern Recognition

Managing 15 acquisitions revealed patterns that informed negotiation strategy:

Customer contracts: 70% of targets had customer agreements with change of control provisions. The team developed a standard consent solicitation process used across all deals.

Equipment financing: 40% of targets had equipment with outstanding liens. The team negotiated a standard payoff process into the purchase agreement template.

Employment agreements: 60% of targets had informal compensation arrangements not documented in writing. The team added employment representation language addressing undocumented commitments.

The Results

Velocity Metrics

| Metric | Before Mage | With Mage | Improvement | |--------|-------------|-----------|-------------| | Average days LOI to close | 75 | 52 | 31% faster | | Simultaneous active deals | 3 max | 8 max | 167% increase | | Deals closed in 9 months | 6 (projected) | 15 (actual) | 150% increase |

Quality Metrics

| Metric | Result | |--------|--------| | Issues discovered post-closing | 0 material | | Integration surprises | Minimal | | Diligence-related price adjustments | 4 deals (appropriate) | | Deals terminated for diligence issues | 2 (appropriate) |

Cost Metrics

| Metric | Before Mage | With Mage | Change | |--------|-------------|-----------|--------| | Outside counsel cost per deal | ~$85K | ~$50K | -41% | | Internal hours per deal | 120 | 80 | -33% | | Total legal cost per deal | ~$110K | ~$70K | -36% |

Integration Benefits

The AI-powered diligence approach provided unexpected integration benefits:

Day-1 contract visibility: All acquired contracts were already extracted and categorized in Mage. Integration teams had immediate visibility into customer terms, vendor obligations, and employment arrangements without re-reviewing documents.

Cross-portfolio analysis: The team could analyze contract terms across the entire acquired portfolio, identifying opportunities for vendor consolidation, customer term standardization, and employment harmonization.

Ongoing contract management: Mage became the contract repository for the combined platform, supporting ongoing operations beyond the initial acquisition diligence.

Lessons Learned

Standardization enables velocity. Using the same extraction framework across all deals meant the team could context-switch between transactions without losing efficiency. Each deal followed the same playbook.

AI changes what lawyers do, not whether you need them. The Senior M&A Counsel remained essential—her time simply shifted from document review to issue resolution and negotiation. AI handled the mechanical work; humans handled judgment.

Volume reveals patterns. Issues that seemed unique on one deal became recognizable patterns across 15 deals. The team developed playbooks for common issues that made subsequent transactions faster.

Integration starts in diligence. Using Mage for diligence meant acquired contract data was immediately available for integration. The traditional approach of re-reviewing contracts post-close added weeks to integration timelines.