How to Run a Due Diligence Checklist on AI tools for Patent Practice for Law Firms

Artificial intelligence for patents has moved from experimentation to a strategic requirement. For law firm partners, innovation leads, and IT professionals evaluating AI software providers like Solve Intelligence, the key question is no longer "does this technology work?" but "Is it secure, compliant, and worth the change management?" 

This article provides a practical, structured due diligence checklist your firm can use to move from abstract concerns about AI risk to a clear, repeatable evaluation that reduces risk, aligns stakeholders, and speeds up internal approval.

Key Takeaways: 

• Structure Your Evaluation: Move from abstract AI concerns to a clear, repeatable, and documented due diligence process to reduce risk and speed up internal approval.
• Security is Non-Negotiable: Demand a contractual commitment that client data is never used to train the vendor's models, and require third-party certifications like SOC 2 and ISO 27001.
• Define Ethical Boundaries: Confirm the tool acts only as an assistant, not an inventor, and establish clear policies for attorney supervision and client disclosure of AI usage.
• Assess Strategic Fit: Evaluate the tool's genuine fit into your firm's specific workflows, its governance controls (customisation, permissions), and the vendor's long-term stability and roadmap.
• Mandate a Structured Pilot: Run a time-boxed pilot (e.g., 4–8 weeks) with a structured scorecard to gather data-backed feedback before formal sign-off by partners and IT/risk teams.

Before comparing tools, define “why you need one.”

This essential first step is about clearly defining your objectives and stakeholders. It involves three key actions:

1. Define your AI Objectives by articulating the specific challenge you are addressing (e.g., reducing drafting time or improving prior art search quality);
2. Define the Scope of Use to decide which initial workflows will be in scope for your first tool (e.g., drafting or prosecution, Freedom-to-Operate analysis, or other claim charting workflows); and
3. Map Stakeholders by ensuring all relevant groups — partners, IT/Security, and Knowledge Management — are involved to secure firm-wide buy-in.

This focused process ultimately produces a short internal brief, which will serve as the clear benchmark against which all vendor conversations and due diligence are measured.

Security, confidentiality, and data handling checks

AI adoption is inseparable from your professional duty of client confidentiality. Rigorous, verifiable compliance is a non-negotiable requirement.

• Data Usage: Demand a contractual commitment that client data and work product are never used to train the large language models used by vendor's models. 
• Data Storage: Confirm server locations and data residency options to match client (US or Europe, for example) or regulatory requirements. Data encryption in storage (and in transit) is vital in maintaining the highest security standards appropriate for patent practice.
• Technical Safeguards: Verify strong encryption (in transit and at rest), robust access controls (SSO, MFA), options for Enterprise SSO (e.g., Azure or Okta), and comprehensive audit logs. Ensure there are features in the software that allow practitioners to maintain oversight of AI output and make validation of AI output easy.
• Policies and Certifications: Request third-party certifications. At a minimum, look for SOC 2 Type II, ISO 27001, and compliance with GDPR and CCPA. Vendors serving patent teams should also demonstrate zero data retention agreements with their LLM providers – see model training—provisions Solve Intelligence includes in its standard Terms of Service

This step, as part of your due diligence checklist, provides a clear "yes" or "no" on whether the tool and vendor fundamentally meet your firm’s security and data confidentiality threshold.

Legal, Ethical, and Professional Responsibility Review

AI must serve as an enhancement to expertise, not a substitute. Professional standards require that an AI-assisted work product meet the same quality threshold as work produced without it.

Therefore, you must confirm the tool is designed only as an assistant or copilot solution. Check how the platform supports your professional duties around confidentiality, privilege, competence, and proper supervision of patent practitioners using the technology. 

The goal of this review is to gain comfort that the tool aligns with your ethical and regulatory obligations, with accountability remaining clear and firmly with the attorney.

In the US, guidance from the ABA and the USPTO increasingly requires attorneys to understand the technology they use, and several bar associations have issued formal opinions on AI disclosure. In Europe, EPI guidelines and GDPR impose distinct data handling requirements. Firms with transatlantic or global practices should verify the vendor supports jurisdiction-specific templates and data residency options for both USPTO and EPO workflows.

Product Capabilities and Workflow Fit

The most valuable tool is one that genuinely fits into your firm’s day-to-day work. When assessing this, consider the following: 

• Inputs and Context: What types of inputs does it accept (e.g., disclosures, manual input of claim sets, prior art), and how well does it handle unstructured data?
• Drafting Workflow: What control do you have over structure, style, and boilerplate language? Can you easily switch between manual and AI-assisted drafting? Does the software support cross-jurisdictional drafting?
• Prior Art and Prosecution: Does the tool support claim charting, prior art search, and office action responses? Which jurisdictions are supported?
• Review Features: Does it include built-in checks (e.g., antecedent basis, consistency, and support) and the ability to cross-check against the original disclosure?
• Claim Charting: Does the tool allow for a variety of workflows required, e.g., Freedom-to-Operate analysis, 102 and 103 invalidity analysis, European Oppositions, portfolio mapping to commercial products?

Assess the tool's coverage of your core patent workflows. Can it generate full specifications from invention disclosures? Does it support claims drafting with proper dependent claim structures? How does it handle figure-description alignment and element labeling—a common source of 112 rejections? For prosecution, evaluate office action analysis, response generation, and claim charting for FTO and invalidity work.

By evaluating these points, you will form a clear view of whether the tool genuinely improves your firm’s specific workflows.

Customization and Governance

To manage AI as a firm-wide capability, you must evaluate the vendor's ability to support your internal standards. This includes Customization, where you can create and maintain firm-wide templates and configure the system around specific client portfolios and style guides. Assess Governance Controls like role-based permissions, review and approval workflows, and the ability to standardize prompts across the team.

With this assessment, you should maintain the confidence that the tool can be managed and deployed consistently as a firm capability.

Additionally, firms should determine whether the vendor has support to help their firm adopt AI effectively. Solve Intelligence has a dedicated support function of Legal & Product Engineers to support your firm's adoption of AI. For example, the Solve Intelligence team actively supports its customers through CLE Presentations. Solve Intelligence's team includes licensed attorneys who are experts on the use of AI, ethical considerations, USPTO + EPO + ABA guidance on AI usage, and more.

Commercial Terms, ROI, and Adoption Support

Connect the contract terms to the expected return on investment (ROI).

• Pricing Structure: Is it per user, per matter, or usage-based? How are volume discounts and different roles handled?
• Measuring Value: Define clear success metrics to track: time saved per draft, reduced rework, and ability to take on more work without increasing headcount.
• Onboarding and Support: Request a clear implementation plan (including SSO/security sign-off), training for pilot and rollout groups, and ongoing customer success education.

This is all about establishing a clear link between contract terms, expected ROI, and the support you will receive after signing.

Vendor Stability and Technical Foundations

Adopting AI is a commitment to a partner in innovation, requiring reassurance about their long-term viability. Review the Company Profile, including the background of the team (especially patent practitioners), investors, and existing customers/case studies. Understand the Technology Stack, specifically the underlying AI models and how the platform provides a specialist layer tailored for patent law, distinguishing it from a private general-purpose LLM. Finally, assess the Roadmap and Responsiveness—how feature requests are prioritized and how frequently the product is updated. 

The final outcome is the reassurance that you are partnering with a stable, specialist vendor with a roadmap demonstrating long-term viability.

Run a Structured Pilot and Formal Sign Off

A successful rollout requires a structured pilot phase to refine initial processes and document the final decision for adoption.

• Structured Design: Define clear success metrics and time-box the pilot (e.g., 4–8 weeks) with a defined group of users across relevant practice areas.
• Data-backed Feedback: Capture and evaluate user feedback using a structured scorecard aligned to your full due diligence checklist items.
• Formal Sign-off: Conclude the process by formally documenting the decision to proceed, expand, or stop after a review with partners, IT, and risk teams.

The pilot’s ultimate goal is to provide a clear, data-backed decision for moving forward, along with the foundational internal policies and training materials needed for a full firm-wide launch. For more details on the transition to full enterprise adoption, read: Solve's Guide for AI Adoption and Firm Rollout.

Conclusion

Due diligence on IP-focused AI tools is a strategic investment in how your firm works in the future. Firms that adopt AI with intention, transparency, and careful governance are best positioned to realize a sustainable competitive advantage. A due diligence checklist should fit the firm’s needs and be used across all vendors for consistent comparison.

At Solve Intelligence, we work with IP professionals to integrate AI into daily practice in a way that preserves quality, accountability, and client trust. Solve Intelligence’s AI platform for patent drafting, prosecution, and claim charting is trusted by 700+ IP teams, including DLA Piper, Siemens, Finnegan, and Perkins Coie.

Ready to evaluate Solve Intelligence against this checklist? Book a demo with our team, or contact partnerships@solveintelligence.com to discuss a structured pilot for your firm.

FAQs

• What is the purpose of a patent AI due diligence checklist?
  It provides a structured process for law firms to evaluate the safety, compliance, ethical alignment, and workflow fit of a patent AI vendor before wide-scale adoption.
  
  
• Who should own the due diligence process inside the firm?
  Ownership should be cross-functional, and this depends on firm structure, procurement experience, and culture, often involving partners, IT/Security , and innovation/knowledge management leads.
  
  
• Do we need client consent to use AI tools on their matters?
  Professional guidance increasingly suggests firms should understand and document client preferences, often requiring disclosure and clarity in engagement letters. Bringing a vendor to educate clients on their solution can help establish trust and credibility.
  
  
• How long should a pilot of a patent AI tool run?
  A pilot should be time-boxed for maximum engagement and to minimize disruption to day-to-day work. For smaller teams, 2 weeks can be an effective timeframe to establish a benchmark for the AI tool and vendor service. For larger firms, this can usually be 4–8 weeks for an in-depth evaluation across practice groups. This is sufficient to cover a full workflow cycle and gather statistically meaningful data. 
  

• How often should we revisit our due diligence after a tool is adopted?
  Annually or whenever there is a significant change to the vendor’s technology, data handling policy, or the global landscape of AI software providers for patents.