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Private Equity Firms

Private Equity firms evaluate hundreds of deals annually, each requiring extensive due diligence. The Discovery Agent transforms this bottleneck into a competitive advantage.

The Problem

A typical PE firm's due diligence process:

  1. Data Room Review: 2-4 analysts spend 4-6 weeks reading thousands of documents
  2. Risk Identification: Manual flagging of critical clauses (Change of Control, MAE, etc.)
  3. Deal Analysis: Extracting financial metrics, contract terms, and operational data
  4. Reporting: Compiling findings into investment committee presentations

Cost: $150K-$300K per deal in analyst time. Time: 6-8 weeks per deal. Limitation: Can only evaluate 8-12 deals per year.

The Solution: Discovery Agent

A Discovery Agent built on Google Vertex AI that:

  • Reads and understands every document in a data room
  • Extracts critical clauses and financial terms
  • Identifies risks and flags them for human review
  • Generates structured reports for investment committees
  • Operates 24/7/365 inside your Google Cloud infrastructure

Implementation Steps

Step 1: Infrastructure Setup

Deploy to your Google Cloud project:

# Create Vertex AI Workbench instance
gcloud notebooks instances create discovery-agent \
  --location=us-central1 \
  --machine-type=n1-standard-4

# Configure access controls
gcloud projects add-iam-policy-binding YOUR_PROJECT_ID \
  --member=serviceAccount:discovery-agent@YOUR_PROJECT_ID.iam.gserviceaccount.com \
  --role=roles/aiplatform.user

Step 2: Agent Configuration

Define the agent's capabilities:

  • Document Types: M&A agreements, financial statements, contracts, legal documents
  • Extraction Targets: Change of Control clauses, Material Adverse Effect terms, financial metrics
  • Output Format: Structured JSON reports with risk scores

Step 3: Training & Validation

  • Train on historical deal documents (anonymized)
  • Validate against known outcomes
  • Establish confidence thresholds for human review

Step 4: Deployment

  • Deploy to production with human oversight
  • Monitor performance and accuracy
  • Iterate based on feedback

Real-World Example: Gold Coast Financial

Before: 4 analysts, 6 weeks, $200K per deal. Evaluated 10 deals/year.

After: Discovery Agent processes data room in 12 minutes. Analysts review flagged items (2-3 hours). Evaluated 50 deals/year.

ROI:

  • Time: 6 weeks → 12 minutes (99.9% reduction)
  • Cost: $200K → $5K per deal (97.5% reduction)
  • Deal Capacity: 10 → 50 deals/year (5x increase)

Key Features

Automated Risk Detection

The agent identifies:

  • Change of Control provisions
  • Material Adverse Effect clauses
  • Financial covenants and triggers
  • Regulatory compliance issues
  • Operational red flags

Structured Data Extraction

Automatically extracts:

  • Financial metrics (EBITDA, revenue, margins)
  • Contract terms (duration, renewal, termination)
  • Key dates (closing, milestones, deadlines)
  • Entity relationships (subsidiaries, partnerships)

Human-in-the-Loop

Critical decisions require human approval:

  • Risk scores above threshold → Human review
  • Ambiguous clauses → Flagged for analyst
  • Financial anomalies → Escalated to CFO

Security & Compliance

  • Data Isolation: All processing happens in your private Google Cloud environment
  • Access Controls: Role-based permissions ensure only authorized access
  • Audit Trail: Every action logged for compliance review
  • Encryption: Data encrypted at rest and in transit

Expected Results

  • Processing Time: 6 weeks → 12 minutes
  • Cost per Deal: $200K → $5K
  • Deal Capacity: 5x increase
  • Accuracy: 95%+ on standard document types
  • ROI: 40x return in first year

Next Steps

  1. Schedule a Shadow Protocol audit to map your current process
  2. Review our Financial Services industry blueprint
  3. Contact Adaven to begin implementation