Agentic AI for Due Diligence in Private Equity

Sample Automated Due Diligence Workflow

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A typical private equity due diligence workflow spans multiple stages, from initial document intake to final investment committee reporting. Underwriters and investment professionals are responsible for:

• Ingesting CIMs, financial statements, management presentations, and legal documents
  
  
• Screening opportunities against the firm’s investment thesis
  
  
• Conducting financial, commercial, and risk diligence
  
  
• Synthesizing findings into valuation assumptions and deal structures
  
  
• Producing investment memos and supporting reports
  
  

This is a representative workflow. Multimodal’s agentic AI can adapt to your team’s specific systems, steps, and priorities. 

AgentFlow is designed to mirror how your underwriters already work, while augmenting each stage with configurable AI agents. Below, we break down how each step of this workflow can be automated and augmented using AgentFlow.

Automating Due Diligence With AgentFlow

1. Initial Screening

Before AgentFlow:
Underwriters manually review incoming CIMs, financial statements, and presentations. Documents arrive in different formats and levels of completeness. Teams spend significant time classifying files, extracting key metrics, and determining whether an opportunity merits deeper diligence.

With AgentFlow:
Using Document AI and Unstructured AI, AgentFlow automatically classifies incoming documents, extracts structured data, and normalizes financial and operational information across sources. Key sections, such as revenue breakdowns, customer concentration, and historical performance, are identified and summarized for underwriter review.

Value:
Faster initial triage, consistent data extraction across deals, and reduced manual effort during early screening, allowing teams to focus attention on the most relevant opportunities.

2. Financial and Commercial Due Diligence

Before AgentFlow:
Once a deal passes screening, underwriters analyze financial statements, validate assumptions, review market research, assess risks, and reconcile data across spreadsheets, internal databases, and third-party sources. This work is time-intensive and often duplicated across team members.

With AgentFlow:
AgentFlow orchestrates multiple agents across this stage. Database AI retrieves and reconciles financial and market data. Conversational AI allows underwriters to query documents and datasets in natural language. Decision AI evaluates risk factors, highlights anomalies, and produces structured assessment summaries aligned with firm-specific criteria.

Value:
Accelerated analysis, fewer reconciliation errors, and clearer visibility into risk drivers, while preserving underwriter judgment through explainable, reviewable outputs.

3. Final Evaluation and Investment Decision

Before AgentFlow:
Investment teams manually consolidate diligence findings into valuation models and decision materials. Sensitivity analyses, scenario comparisons, and deal structure considerations are assembled across disconnected tools, increasing the risk of inconsistency.

With AgentFlow:
Using Decision AI, AgentFlow synthesizes diligence outputs into structured recommendations, supports sensitivity analysis, and maps assumptions directly to source data. Confidence thresholds and escalation rules ensure that final decisions remain firmly under human control.

Value:
Cleaner decision inputs, faster investment committee preparation, and improved confidence in final evaluations, without introducing black-box automation.

4. Reporting

Before AgentFlow:
Underwriters and associates manually draft investment memos, valuation reports, and internal summaries. Reporting often requires repetitive formatting, cross-checking numbers, and validating references back to original documents.

With AgentFlow:
Report AI automatically generates structured reports and memos using validated outputs from earlier workflow stages. Every figure, summary, and conclusion is traceable to its source, with audit-ready documentation preserved throughout.

Value:
Reduced reporting time, consistent memo quality, and improved auditability, freeing teams to focus on deal strategy rather than document production.

How AgentFlow Ensures Security, Governance, and Trust in Due Diligence

Private equity due diligence demands strict controls over sensitive financial and legal information. AgentFlow is built to support regulated, high-stakes workflows with enterprise-grade safeguards.

AgentFlow deploys within your private VPC, on-prem environment, or single-tenant setup, ensuring data sovereignty, your data never leaves your walls. 

Governance is embedded at the workflow level. Every AI action is logged, versioned, and traceable to predefined business rules. Role-based access controls, confidence thresholds, and human-in-the-loop escalation ensure AI augments, (not replaces), underwriter judgment. 

AgentFlow maintains immutable JSON audit logs compatible with enterprise monitoring tools, enabling compliance reviews and internal audits.

Ready to Adapt Agentic AI to Your Due Diligence Workflow?

The workflow we used for this page is just one example. AgentFlow’s agentic workflow architecture is modular and configurable, allowing private equity firms to tailor each AI agent to their existing processes, investment criteria, and systems.

Whether your team focuses on platform acquisitions, add-ons, or sector-specific strategies, AgentFlow adapts to how you work today while supporting how you scale tomorrow.

Book a demo to see how AI due diligence for private equity can become faster, more consistent, and more transparent, with AgentFlow supporting your team at every step.

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