The Best AI Tools for Due Diligence: Streamlining Your Investigation Process
In today’s fast-moving business environment, thorough due diligence is essential. Whether you are evaluating a merger or acquisition, onboarding a strategic vendor, or assessing a new investment, you need a clear view of the risks, obligations, and opportunities tied to the target organization.
Traditionally, due diligence has been time-consuming, document-heavy, and prone to missed details. AI tools now help legal and business teams review larger volumes of information faster, extract key terms, and surface potential issues earlier in the process.
This guide covers the best AI tools for due diligence, what each one is best suited for, and how to choose the right option for your workflow.
Why AI-Powered Due Diligence Matters
Due diligence often involves reviewing large collections of contracts, financial statements, internal reports, correspondence, regulatory filings, and other records. Manually sorting through that material can be slow and resource-intensive.
AI helps teams:
- Accelerate review by processing large data sets faster than manual methods
- Improve accuracy by identifying patterns, anomalies, and missing information
- Extract insights from unstructured data such as contracts, emails, and reports
- Surface risks earlier, including compliance issues and contractual obligations
- Free up professionals to focus on judgment, strategy, and client communication
Used well, AI does not replace due diligence expertise. It makes that expertise more efficient and more scalable.
The Best AI Tools for Due Diligence
1. RelativityOne
What it does: RelativityOne is a cloud-based e-discovery and analytics platform that uses AI to review large document sets. It supports technology-assisted review, clustering, conceptual search, and document classification.
Why it is useful: RelativityOne is well suited to handling large-scale due diligence projects. It can reduce the number of documents that require manual review and help teams identify key clauses, obligations, and risk areas across extensive data rooms.
Best fit: Complex M&A transactions, litigation support, and internal investigations involving large document volumes.
Pros:
- Highly scalable
- Strong security features
- Broad analytics and review capabilities
- Well established in the legal market
Cons:
- Can be complex for new users
- May require a meaningful investment
- More document-focused than broad business intelligence platforms
2. Kira Systems
What it does: Kira Systems, now part of Litera, is an AI-powered contract analysis platform. It is designed to identify, extract, and review key provisions in contracts, leases, loan agreements, and other legal documents.
Why it is useful: Kira is especially valuable in contract-heavy due diligence. It can quickly flag change of control provisions, termination rights, assignment language, financial covenants, and other clauses that may affect deal value or risk.
Best fit: M&A due diligence, real estate transactions, and any review involving a large contract portfolio.
Pros:
- Strong contract clause identification
- Efficient for high-volume review
- User-friendly interface
- Supports custom clause training
Cons:
- Focused primarily on contracts
- Less useful for broader sources like emails or memos
- May need to be paired with other tools for a fuller review
3. IBM Watson Discovery
What it does: IBM Watson Discovery is an AI search and content analytics platform that can process structured and unstructured data from documents, websites, and databases. It uses NLP to understand queries, identify entities, and uncover relationships in the data.
Why it is useful: Watson Discovery can support due diligence by pulling together information from financial reports, news, filings, and internal records. It helps teams identify trends, potential compliance issues, and business risks across multiple data sources.
Best fit: Market intelligence, competitive analysis, risk assessment, and compliance review within a broader due diligence process.
Pros:
- Handles varied data types
- Strong NLP and search capabilities
- Enterprise-ready
- Flexible API options
Cons:
- Can be complex to implement
- Often requires data preparation
- Pricing may be high for heavy usage
4. Everlaw
What it does: Everlaw is a cloud-based e-discovery platform with AI features such as predictive coding and clustering. It also includes search, document timelines, and collaboration tools.
Why it is useful: Everlaw helps teams move through large document sets more efficiently while keeping review workflows organized. Its interface is designed to be accessible, making it easier for teams to collaborate during due diligence and investigations.
Best fit: Law firms and in-house legal teams that want a user-friendly e-discovery platform for due diligence, investigations, and litigation support.
Pros:
- Easy to use
- Strong collaboration features
- Efficient document review workflows
- Good search and visualization tools
Cons:
- Less specialized for niche contract analysis
- May not match dedicated contract review tools for clause-level depth
5. AuditBoard
What it does: AuditBoard is a cloud-based platform for audit, risk, and compliance management. It is not a document review tool in the traditional sense, but it uses automation and AI to support risk assessment, controls testing, and compliance tracking.
Why it is useful: In due diligence, AuditBoard can help assess a target company’s internal controls, compliance posture, and risk management maturity. That makes it useful when the review goes beyond contracts and focuses on operational risk.
Best fit: Buyers, investors, and legal teams evaluating internal controls, governance, and regulatory compliance.
Pros:
- Strong GRC capabilities
- Useful for structured risk assessment
- Consolidates multiple risk functions
- Robust reporting features
Cons:
- Not built for granular legal document review
- Better for controls and compliance than clause-level analysis
- May need to be connected to other data sources
6. Casetext CoCounsel
What it does: Casetext’s AI legal assistant, CoCounsel, uses large language model capabilities for legal research, summarization, and drafting support. It can summarize documents, assist with legal research, and help synthesize findings.
Why it is useful: CoCounsel can speed up early-stage due diligence work by summarizing long legal documents, helping review relevant case law, and supporting the drafting of due diligence reports.
Best fit: Legal teams that need faster research, document summarization, and drafting support as part of the due diligence process.
Pros:
- Strong legal-focused AI capabilities
- Fast summarization
- Helpful for research and drafting
- Designed for legal workflows
Cons:
- Requires careful human review
- LLMs can generate inaccurate outputs
- More focused on legal text than financial or operational data
How to Choose the Right AI Tool for Due Diligence
The best AI tools for due diligence depend on the type of review you are conducting and the data you need to analyze. Key factors to consider include:
- Scope of data: Are you reviewing contracts only, or also emails, reports, filings, and financial records?
- Primary risk areas: Are you focused on contractual liabilities, compliance, operational controls, or broader business intelligence?
- User experience: Some platforms are more intuitive than others, which can affect adoption and efficiency.
- Integration needs: Check whether the tool works with your document management system, data room, CRM, or other internal systems.
- Budget: Pricing can vary significantly based on volume, users, and functionality.
- Scalability: Make sure the platform can handle the size and complexity of your expected matters.
In many cases, the strongest approach is a combination of tools. For example, an e-discovery platform like RelativityOne or Everlaw can handle document processing, while Kira can support deeper contract review. CoCounsel can then help with legal research and summarization.
Pricing and Value Considerations
AI due diligence tools vary widely in price depending on features, data volume, user count, and implementation requirements.
Common pricing models include:
- Subscription plans: Monthly, annual, or usage-based pricing
- Per-user pricing: Charges based on the number of active users
- Per-project or volume-based pricing: Based on matters, documents, or data processed
When evaluating cost, also account for setup, data migration, and training. More advanced enterprise tools may require additional implementation support.
The key question is not just what the tool costs, but what it saves. A platform that reduces manual review time, shortens deal cycles, and helps identify a material risk early may provide strong return on investment.
Before committing, ask for a demo or pilot to test the platform on real due diligence materials.
Frequently Asked Questions About AI Tools for Due Diligence
Can AI replace human due diligence professionals?
No. AI is best used to support human expertise, not replace it. It can speed up review and highlight issues, but people still need to interpret findings and make final judgments.
How accurate are AI due diligence tools?
Accuracy varies by tool and use case. Specialized tools can perform very well on tasks like clause extraction, but all AI output should be reviewed by experienced professionals.
What kinds of data can these tools analyze?
Most can process unstructured documents such as contracts, emails, reports, and filings. Many also handle structured data like spreadsheets and databases.
Are these tools difficult to implement?
It depends on the platform. Some are relatively easy to deploy, while others require more setup, configuration, and training.
What are the main challenges in adopting AI for due diligence?
Common challenges include data quality, cost, workflow change, integration, and the need for human oversight.
Are there ethical considerations?
Yes. Data privacy, bias, transparency, and responsible use of AI are all important. Human review remains essential.
Conclusion
AI is changing how due diligence is performed. Instead of manually reviewing every document, legal and business teams can use AI tools to process data faster, identify risks more efficiently, and focus attention on the most important issues.
The best AI tools for due diligence depend on your goals. RelativityOne and Everlaw are strong options for large-scale document review. Kira Systems is well suited to contract-heavy matters. IBM Watson Discovery is useful for broader data analysis. AuditBoard supports risk and compliance review, while CoCounsel helps with legal research and summarization.
Choosing the right platform means matching the tool to the type of diligence you perform, the data you review, and the workflow your team needs. Used strategically, AI can make due diligence faster, more thorough, and more actionable.