The Best AI Tools for Due Diligence in 2024
Due diligence is essential to sound decision-making, but it is often complex, time-consuming, and document-intensive. Whether you are acquiring a company, investing in a startup, assessing risk, or reviewing regulatory compliance, you need to examine large volumes of information carefully and consistently.
Traditionally, this work required legal and business teams to manually review contracts, financial reports, emails, filings, intellectual property records, and other documents. AI tools can streamline that process by extracting information, identifying patterns, flagging potential risks, and prioritizing documents for human review.
The best AI tools for due diligence do not replace lawyers or business professionals. Instead, they reduce repetitive work and help teams focus on judgment, strategy, and risk assessment. This guide examines several widely recognized tools and explains where each is most useful.
Why AI Is Changing Due Diligence
For legal professionals, investors, and business leaders, due diligence provides the foundation for evaluating a transaction or business relationship. An incomplete review can lead to financial losses, regulatory problems, litigation, or reputational damage.
Manual review remains important, but it has practical limitations. Large datasets can overwhelm review teams, and subtle inconsistencies or unusual contractual provisions may be difficult to identify consistently. AI can help address these challenges by:
- **Extracting and organizing data:** AI can scan and categorize information from contracts, financial reports, emails, legal filings, and other unstructured documents.
- **Identifying risks and anomalies:** Machine learning can highlight unusual language, inconsistencies, potential compliance concerns, and other issues for further review.
- **Accelerating contract analysis:** AI can locate clauses, obligations, deadlines, and deviations from standard terms across large contract populations.
- **Supporting legal research:** AI-powered research tools can help surface relevant case law, regulations, and legal authorities.
- **Reducing review time and costs:** Automating repetitive tasks allows legal and business teams to concentrate on higher-value work.
- **Revealing patterns and connections:** AI can identify relationships across documents and datasets that may not be obvious during a manual review.
AI is now a practical component of modern due diligence. However, its results should be reviewed by qualified professionals, particularly when the analysis involves legal interpretation, material transaction risks, or jurisdiction-specific requirements.
The Best AI Tools for Due Diligence
The right platform depends on the type of due diligence being performed. Some tools are designed for large-scale e-discovery, while others focus on contracts, legal research, or intellectual property.
1. RelativityOne
What it does
RelativityOne is a cloud-based e-discovery and document review platform that uses artificial intelligence and machine learning to help teams identify, organize, and analyze large volumes of electronic data.
Its capabilities include technology-assisted review, document classification, clustering, advanced search, case management, analytics, and data visualization. Technology-assisted review can learn from reviewer input and help prioritize documents for further examination.
Why it is useful for due diligence
RelativityOne is well suited to due diligence projects involving extensive electronic data. It can help teams locate relevant documents, identify potentially problematic information, and establish a structured review process.
The platform’s scalability, audit trails, and review controls can also support defensible workflows in transactions, investigations, litigation, and regulatory matters.
Best use cases
- Large-scale mergers and acquisitions
- Internal investigations
- Regulatory reviews
- Litigation related to a transaction
- Due diligence involving millions of emails and other electronic documents
Pros
- Highly scalable
- Strong e-discovery and analytics capabilities
- Robust security and audit features
- Supports technology-assisted review
- Broad integration options
- Suitable for complex legal workflows
Cons
- Can have a significant learning curve
- May be expensive for smaller firms or individual users
- Often requires training and dedicated implementation support
2. Kira Systems
What it does
Kira Systems is an AI-powered contract analysis platform designed to help legal teams extract and review provisions from contracts and other legal documents. It uses machine learning to identify and categorize clauses, terms, and other data points across large document sets.
The platform includes prebuilt provisions for common contract types and allows users to create custom provisions for specific review requirements.
Why it is useful for due diligence
Contract review is central to many due diligence projects. Kira can help identify provisions such as:
- Change-of-control clauses
- Termination rights
- Indemnification obligations
- Assignment restrictions
- Financial covenants
- Renewal provisions
- Exclusivity requirements
- Key commercial obligations
By automating the first pass of contract review, Kira can improve consistency and help legal teams focus their attention on unusual or high-risk provisions.
Best use cases
- M&A contract review
- Commercial real estate due diligence
- Loan portfolio analysis
- Master service agreement reviews
- Identifying obligations and risks in commercial contracts
Pros
- Specialized contract analysis capabilities
- Supports provision identification and categorization
- Designed for legal professionals
- Useful for reviewing large contract populations
- Allows custom analysis workflows
Cons
- Primarily focused on contracts
- Less suitable for broad non-legal data analysis
- Licensing and implementation may require a substantial investment
3. Luminance
What it does
Luminance is an AI platform for legal document review, contract analysis, due diligence, discovery, and compliance work. It uses AI to analyze legal language, classify documents, identify clauses, compare agreements with standard templates, and flag potential risks.
Why it is useful for due diligence
Luminance can help teams quickly understand a large collection of legal documents and focus on provisions that require closer review. It may identify unusual language, deviations from expected terms, and inconsistencies across agreements.
Its dashboards and document organization features can also provide a clearer overview of the review population and outstanding issues.
Best use cases
- M&A due diligence
- Contract portfolio reviews
- Property portfolio analysis
- Large-scale legal document review
- Compliance and regulatory projects
Pros
- Strong focus on legal language
- Supports clause analysis and document classification
- Can identify deviations and potential risks
- Useful visualization and review features
- Designed for legal workflows
Cons
- Primarily suited to legal documents
- Less appropriate for broad financial or operational analysis
- Pricing may be a barrier for smaller organizations
4. Casetext and CARA AI
What it does
Casetext was a legal research platform that included AI features such as CARA AI. CARA AI allowed users to upload a brief or legal document and identify potentially relevant cases, statutes, and other authorities based on the document’s legal arguments and citations.
The platform’s AI capabilities also supported aspects of document review and contract analysis.
Because legal technology products and ownership can change, users should confirm the current availability and functionality of Casetext and CARA AI before relying on them for a new project.
Why it is useful for due diligence
Legal due diligence often requires more than reviewing the target’s documents. Teams may also need to understand relevant legal authorities, regulatory requirements, and litigation risks.
AI-assisted legal research can help identify authorities related to a transaction issue or legal argument. This may support the assessment of regulatory exposure, potential disputes, and legal risks associated with an acquisition or investment.
Best use cases
- Legal risk assessments
- Researching case law related to an acquisition
- Regulatory compliance reviews
- Assessing potential litigation exposure
- Reviewing legal arguments and cited authorities
Pros
- AI-assisted legal research
- Helps analyze citations and legal arguments
- Useful for finding related authorities
- Designed for legal professionals
Cons
- Primarily focused on legal research
- May not provide the same depth of contract analysis as specialized contract platforms
- Current availability and product features should be verified
5. Lumin AI by Logikcull
What it does
Lumin AI is described as an AI-powered document review solution associated with Logikcull’s e-discovery platform. It uses machine learning to help categorize documents, identify relevant information, detect anomalies, and support early case assessment.
Why it is useful for due diligence
Lumin AI can be useful when a due diligence project involves finding specific document types, communications, keywords, or patterns in a large dataset. It may help identify transaction-related materials, organize potentially problematic communications, and prioritize documents for review.
Its connection to an e-discovery workflow can be helpful when due diligence overlaps with an internal investigation, litigation, or regulatory inquiry.
Best use cases
- Finding key documents in M&A reviews
- E-discovery related to an acquisition
- Compliance investigations
- Internal risk assessments
- Early assessment of large document collections
Pros
- Supports document categorization and anomaly detection
- Integrates with an e-discovery workflow
- Designed for legal review processes
- Can accelerate initial data assessment
Cons
- More focused on e-discovery than deep contract analysis
- May require familiarity with the Logikcull ecosystem
- Product functionality and availability should be confirmed before purchase
6. Brainbase
What it does
Brainbase is described as a platform for managing and analyzing intellectual property portfolios, including patents, trademarks, and copyrights. Its AI-related capabilities can support IP portfolio assessment, infringement-risk analysis, competitive intelligence, and IP due diligence.
Why it is useful for due diligence
Intellectual property can be one of the most valuable assets in a technology or research-focused company. An IP-focused platform can help teams organize and assess patents, trademarks, and other rights while identifying potential conflicts, ownership issues, licensing opportunities, or gaps in protection.
This analysis can contribute to a clearer understanding of the target’s IP value and risk profile.
Best use cases
- IP due diligence for M&A
- Technology investment reviews
- Patent portfolio assessments
- Trademark and copyright portfolio analysis
- Competitive IP research
- Risk analysis for IP-intensive businesses
Pros
- Focused on intellectual property analysis
- Useful for reviewing patent and trademark portfolios
- Can help identify IP-related risks and opportunities
- Well suited to technology and R&D-focused transactions
Cons
- A niche solution
- Not designed for general financial, operational, or contract due diligence
- Users should verify the platform’s current capabilities and availability
How to Choose the Right AI Due Diligence Tool
Selecting the best AI tool for due diligence requires matching the platform to the project, data, team, and risk profile.
Define the Scope of the Review
Start by identifying the primary task:
- Contract review
- E-discovery
- Legal research
- Financial document analysis
- Intellectual property assessment
- Regulatory or compliance review
Kira Systems and Luminance are focused on contract and legal document analysis. RelativityOne and Lumin AI are better suited to large-scale electronic data and e-discovery workflows. Brainbase is designed for IP-focused analysis.
Consider Data Volume and Complexity
A project involving millions of documents or multiple data sources may require a highly scalable platform such as RelativityOne. A narrower review involving a defined set of agreements may be better suited to a contract analysis tool.
Consider whether the platform can handle:
- The expected number of documents
- Multiple file types
- Email and message data
- Scanned or image-based documents
- Multiple languages
- Complex legal terminology
- Data from different jurisdictions
Evaluate Team Expertise
Some tools are designed for relatively focused workflows, while others require more extensive configuration and training. Consider the team’s technical experience, the availability of internal administrators, and the level of vendor support provided.
A platform with a simpler interface may be easier to adopt, but a more configurable system may be better for complex or recurring due diligence work.
Check Integration Options
The tool should fit into the organization’s existing legal technology and document-management environment. Review whether it can integrate with:
- Document management systems
- E-discovery platforms
- Contract lifecycle management software
- Legal research databases
- Identity and access management systems
- Export and reporting workflows
Review Security and Privacy Controls
Due diligence materials often include confidential business information, privileged communications, personal data, and commercially sensitive contracts. Before uploading data, review the provider’s:
- Encryption practices
- Access controls
- Data retention policies
- Audit capabilities
- Hosting arrangements
- Use of customer data for model training
- Compliance commitments
- Deletion procedures
The tool should meet the organization’s contractual, regulatory, and professional obligations.
Compare Features and Total Cost
Useful features may include:
- Clause identification
- Custom extraction models
- Risk flagging
- Anomaly detection
- Document classification
- Search and filtering
- Reporting and dashboards
- Human-in-the-loop review
- Audit trails
- API or system integrations
Many vendors offer demonstrations or trial options. Testing the platform with representative, properly protected documents can reveal whether its output is useful in practice.
Pricing and Value Considerations
AI due diligence tools can range from relatively accessible subscriptions to enterprise platforms with significant licensing, implementation, and support costs. Pricing may depend on the number of users, document volume, features, storage, project duration, or level of vendor assistance.
Common pricing structures include:
- **Subscription pricing:** Often based on users, features, storage, or data volume.
- **Per-project pricing:** Useful for one-time transactions or limited reviews.
- **Per-document or usage-based pricing:** Costs are tied to the number of documents processed or the level of platform usage.
- **Enterprise licensing:** May include custom integrations, security requirements, training, and support.
When assessing value, consider more than the purchase price. The total cost of ownership may include implementation, data preparation, configuration, training, quality control, and ongoing support.
Potential sources of value include:
- Reducing manual review time
- Lowering external legal or consulting costs
- Identifying material risks earlier
- Supporting faster transaction timelines
- Improving review consistency
- Reducing the likelihood of missed issues
- Creating a clearer audit trail
AI should be evaluated based on how well it supports the overall review process, not simply on the number of automated features it offers.
Frequently Asked Questions About AI in Due Diligence
Will AI replace lawyers in due diligence?
No. AI is generally used to assist legal and business professionals rather than replace them. It can automate repetitive tasks, prioritize documents, and identify potential issues, but lawyers and other qualified professionals must still interpret the results, assess legal significance, resolve ambiguities, and advise clients.
How accurate are AI tools for legal document review?
Accuracy varies by tool, task, document quality, language, and data set. A platform may perform well at identifying a defined contract provision but be less reliable when interpreting ambiguous language or unusual drafting.
Human review remains important for validating results, handling exceptions, and assessing the significance of identified issues. Teams should test the tool using representative documents and establish a quality-control process before relying on its output.
Are AI due diligence tools secure?
Leading providers typically offer security measures such as encryption, access controls, audit logs, and data protection programs. However, security standards differ between vendors.
Before selecting a tool, confirm how the provider stores, processes, retains, and deletes data. Organizations should also determine whether customer data is used to train models and whether the platform meets applicable confidentiality, privacy, and regulatory requirements.
How difficult are these tools to learn?
The learning curve depends on the platform and the scope of the project. Focused contract analysis tools may be easier to adopt than comprehensive e-discovery systems, which can require configuration, training, and dedicated administration.
Vendor training, implementation support, documentation, and a clear internal review process can improve adoption.
Can AI tools handle multiple languages and jurisdictions?
Some platforms support multiple languages and can be configured for different legal and regulatory environments. Capabilities vary, however, and performance may be less consistent across languages or jurisdictions.
For cross-border due diligence, confirm the tool’s language support, legal content coverage, data-hosting options, and ability to handle jurisdiction-specific terminology and requirements.
Conclusion
AI tools can make due diligence faster, more organized, and easier to scale. Contract analysis platforms such as Kira Systems and Luminance can help identify important provisions across large agreement sets. RelativityOne and Lumin AI are suited to extensive electronic data and e-discovery workflows. Legal research tools such as Casetext and CARA AI can support analysis of legal authorities, while Brainbase is designed for intellectual property-focused reviews.
The best AI tool for due diligence depends on the type and volume of data, the required features, the team’s expertise, the organization’s security requirements, and the available budget. AI should support—not replace—professional judgment. With appropriate testing, oversight, and quality control, it can help legal and business teams reduce repetitive work, identify risks earlier, and make better-informed decisions.