News and analysis dedicated to the commercial litigation funding sector including regulatory issues, case developments, funding activities, and more.

Below is our LFJ Conversation with T.J. Wolf, General Counsel & Senior Expert at DisputeSoft.

T.J. Wolf is DisputeSoft’s General Counsel and supports the firm’s testifying experts as a Senior Expert, where his background in transactional law and intellectual property allow him to become involved in a variety of matters involving root cause analysis of IT failure and intellectual property matters. Mr. Wolf holds a JD from the University of Dayton School of law, and an LLM in Intellectual Property Law from The George Washington University Law School. His professional career has been driven by a deeply personal passion for technology, innovation and creative arts, and an inspiration to pursue a career that supports those who create and innovate. Mr. Wolf has been with DisputeSoft for over ten years, where his knowledge and experience has greatly influenced strategic approaches and expert analysis across numerous matters.

Prior to joining DisputeSoft, Mr. Wolf gained practical legal experience as an academic researcher and law clerk for attorneys in various practice areas, including intellectual property transactions, government contracting, compliance, and entrepreneurship. As a staff writer for the University of Dayton Law Review, Mr. Wolf provided editorial support for the journal and independently researched and authored legal commentary addressing copyright issues presented by emerging technologies. His written work, Becoming Unplugged: Without a Compulsory License, Internet Broadcast Television Powers Down, received an award given annually to the author of the best student-written work. As an LLM candidate at The George Washington University Law School, Mr. Wolf authored a master’s thesis further addressing a persistent International copyright issue known as the orphan works problem: Building an Orphanage: How Judicial Authority Can Provide Immediate Relief to the Copyright Orphan Works Problem.

To set the stage, tell us about DisputeSoft and your own path to founding it. What does the firm actually do across source code examination, IT project failure and IP disputes, and why is strict neutrality so important?

DisputeSoft is an internationally recognized software disputes expert witness firm, providing testimony and litigation support to litigators in software disputes. The firm values intellectual honesty, analytical rigor, technical mastery, and collaboration – internally and externally with its litigation partners – to solve complex technical problems.

DisputeSoft was founded in 2003 as Jeff Parmet and Associates, LLC. In 2012, the firm adopted the market presence and doing business as “DisputeSoft.” The DisputeSoft brand is now well-recognized in the United States and internationally, and is a federally registered trademark. In 2021, DisputeSoft was wholly acquired by Taron, LLC, a Maryland enterprise, with no change to DisputeSoft’s brand identity, market presence, and strength of its expert witness capabilities.

DisputeSoft’s clients include Big Law firms, boutique practices, and sole practitioners who represent Fortune 100 companies, government clients, and corporate and individual clients across all industries in disputes involving software failure, intellectual property, and computer forensics.

Our experts apply decades of real-world software development and implementation experience to investigate produced systems and documents, reach findings and opinions, prepare expert reports, and defend opinions at deposition and trial.

Based in the Washington, D.C. metropolitan area, our practice is international in scope, with clients located across Australia, Canada, Cayman Islands, New Zealand, and major U.S. cities such as Austin, Boston, Chicago, Dallas, Los Angeles, Miami, New York, San Francisco, and Washington DC.

DisputeSoft has served as an expert witness in disputes before state and federal courts, including the U.S. Court of Federal Claims (CFC), U.S. International Trade Commission (ITC), and U.S. Patent Trial and Appeal Board (PTAB), and before U.S. and international arbitration panels.

DisputeSoft offers rigorous software expert witness services to parties dealing with software related legal disputes, often working as an extension of the litigation team in those matters, helping investigate and understand material technical facts that relate to the underlying legal issues.

Across source code examination, IT project failure and IP disputes, we conduct specialized technical analysis to identify the material facts of a dispute, then use those facts to develop expert opinion testimony. In nearly any civil litigation context, we discover defensible facts that drive outcomes. We also understand that technical facts by themselves are insufficient, and what is also needed is their appropriate contextualization to support defensible expert opinions. We perform technical analyses to understand the facts and their materiality, and rely on those facts to understand the events behind the dispute. Drawing on our collective experience, we reduce that material to expert opinions that tell a defensible technical story.

Strict neutrality is vital for several reasons. The most important is the role an expert witness plays in litigation, which is largely to educate the judge and jury about subject matter beyond common knowledge. In our area of expertise, that subject matter is software development and implementation, and the best practices of the software industry.

In some of these matters, for example, discrete concepts relating to software architecture and software design are key to the legal issues, but a judge or jury is unlikely to be familiar enough with that subject matter to render a fully informed decision on nuances that are apparent to practitioners. Simply put, without first learning the material, there is a risk that any resulting decision is simply incorrect as it relates to the industry or stands in opposition to common industry practices.

This concept is a pillar in our understanding of the judicial system, that judges and juries are charged with a duty to deliver justice where due, and justice cannot be achieved if the facts and what they mean in context are not thoroughly understood.

The second reason neutrality is important in these contexts has to do with the role experts play within a litigation team. Though it is most often that expert witnesses are retained to support one side of a dispute, the expert’s role is to deal with the facts head on, even when they may be unfavorable to the client. In an adversarial context, there are advantages to emphasizing or de-emphasizing certain facts, and an attorney seeking the best outcome for a client has a motivation to do so. In part, that’s what we’ve come to understand as a significant component of effective lawyering, is one’s persuasive use of the facts. An expert, on the other hand, confronts those facts head on adhering to the neutrality principle, serving as a voice of objectivity within the legal team. This helps the litigation team deal with those facts more directly and effectively because they are aware of them, understand what they mean as they pertain to the litigation matter and within the software industry, and can strategize as to how to put them to proper use.

When a software copyright, trade secret or failed-implementation claim crosses a funder’s desk, what separates a genuinely fundable matter from one that merely looks good on paper?

Genuinely fundable matters tend to be those that have strong merits identified through a preliminary pre-litigation assessment. From our perspective, what makes those types of matters fundable is a combination of strength of the merits and the time available to be thorough. If there has been a pre-litigation investigation that suggests a significant likelihood of success on a claim, and that preliminary investigation is reasonably thorough, it could signal a higher likelihood of success. That still carries the risk that the claims are defeated, since litigation strategy is often unique to the matter and pre-litigation investigations may be missing a material piece that emerges only in discovery.

As mentioned, the second element is the time available to facilitate thoroughness. Litigation, especially software litigation, involves highly complex technical questions and large volumes of record data that require expert analysis to discover the underlying issues. We’ve typically encouraged litigators to engage expert witnesses as early as possible or reasonable as a result. The more time an expert has to consume and analyze the potentially relevant materials, the more thorough and defensible an expert’s testimony can be.

Walk us through what expert involvement looks like over the life of a funded software matter. When should an expert come in, and how should a funder budget for it relative to matter size?

Our general recommendation is to involve experts as early in the matter as is reasonably and financially possible. As we’ve seen in numerous matters throughout our company history, the earlier we can be involved in the lifecycle, the better the outcome can be. This is because our knowledge and experience are rooted in the software industry as it is practiced, and not strictly as it is litigated. Involving experts early in the process helps in very significant ways, such as identifying and preserving systems of record through forensic techniques to ensure potentially relevant information is shielded from destruction, corruption, or loss, and preparing requests for production that are highly specific and targeted to capture as much of the record data as possible. In turn, this provides a more robust volume of information into which we can investigate to find material facts.

As we’ve discussed on our website, and have described throughout many expert reports submitted in federal and state courts, software systems can be incredibly complex across numerous distinct systems of record, machines, databases, etc., each containing their own set of data that tells a different part of the story. Engaging an expert witness early in a dispute helps to capture as much of that historical record as possible for thorough investigation before it’s too late. Further, the analyses that underscore many of these disputes are in themselves complex and nuanced, and early engagement supports more deliberate, careful, and methodical consideration of the entire scope of information. This tends to lead to better outcomes because the longer an expert has available to them to analyze these complex systems and deploy these complex analytical techniques, the more time that expert has available to refine the analyses and reach more defensible conclusions about what is found.

Further, with more available time, an expert can delegate some of the analytical work to more cost-effective analytical resources in a much more controlled and strategic way. For example, consider a scenario in which an expert’s analysis would typically take two full working weeks of their time to complete. In a rushed scenario, the lead expert is likely doing close to 100% of the analytical work and pushing toward a fast-approaching deadline at a high price. Comparatively, if that same analysis were spread over two months, and the lead expert can delegate some of the effort to junior resources who may be capable of completing the same analysis in approximately the same time operating at the direction and discretion of the lead expert, the cost trends downward. In this second scenario the expert is never fully disengaged from the analysis, but provides direct input and oversight to ensure the analysis is performed properly and thoroughly and will be completed on time without the same financial or time pressures.

This is not to say that an expert retained late in the process isn’t as effective. In fact, we’ve had numerous matters where late-stage engagement has been successful, but those scenarios tend to involve very limited questions and analysis and involve smaller volumes of information to consider. However, hindsight tends to indicate that earlier engagement could have brought an earlier resolution to the issues, and avoided certain unnecessary costs, or identified additional potentially relevant systems of record for more thorough analysis to strengthen the case at an earlier phase.

You have written on AI-generated code in copyright and trade secret disputes, on the USPTO’s inventorship guidance, and on the Anthropic settlement. How is AI-generated material changing what a technical expert can and cannot prove about copying and provenance, and what should a funder understand about that evidentiary risk before backing one of these claims?

As is the case with many software related disputes, what can be established from data, communications, and development artifacts can be highly dispositive or persuasive. When it comes to AI-generated code specifically in copyright and trade secret matters, the available data, communications, and development artifacts may not be as heavily or thoroughly documented, and so establishing facts is not only more difficult but can be less persuasive.

A funder should understand that AI-generated material incorporated into copyright or trade secret material is not an inherently negative or positive attribute, but does provide an additional degree of difficulty in performing the investigation. For example, consider when AI generated code generated from one of the popular generative AI platforms is subsequently used in a large enterprise software application. An expert focusing on understanding the development history of the resulting code may not be able to construct a thorough development history, or may construct one that has either gaps or peculiarities that will likely signal to the opposing side specific areas for their rebuttal. As software is often developed by writing code and committing it to source code repositories, which record metadata about what was written and when, AI generated code may confound the record, making it more difficult to prove or refute certain questions or issues.

The particular risk presented by one of these claims is that the facts become more difficult to understand. Whether a particular block of code originates from a person or from a generative AI output is a factual question that has significant implications in light of recent developments, including whether that AI output is a copy of another work, or whether that AI output is eligible for the relevant legal protection. To put it simply, a funder should understand that AI-generated material presents an additional layer of complexity and risk with respect to the outcome of a matter, and one that cannot be easily anticipated or mitigated.

You have begun offering pre-litigation mediation and pre-suit expert support. Where does an early technical read change the economics of a matter, and where should funders be positioning now?

The short answer is that it depends, because there are a few economic tradeoffs at play in any litigation context. Generally, facts known today that can be used are better than facts learned tomorrow. Consistent with our standard recommendation, we tend to encourage expert engagement as early as possible in the matter lifecycle. An early technical read on the merits and facts can contribute directly to later cost avoidance. It helps define matter strategy and can drive early settlement discussions. Even where those settlement discussions do not completely resolve all issues, knowing material facts earlier in the matter can potentially narrow the scope of remaining material issues to be investigated and sorted out.

On the flip side, a late technical read on a matter contributes heavily to costs as experts may need to “scramble” to come up to speed and define their analyses, and contextualize the results, under quickly approaching deadlines. This can be costly due to the increased level of effort and attention required to complete the work on time but bears a higher risk that an expert’s analysis has methodological or factual gaps that could have been avoided with more lead time.

In a way, it’s a balancing act in terms of where funders should be positioning themselves. Engaging expert witnesses early in the lifecycle may increase immediate costs to some degree, but the tradeoff is that an early technical read on the matter could sharpen litigation strategy, settle some or all of the material issues in the matter, and potentially shorten the entire matter lifecycle. Alternatively, it could expose the most challenging areas to be addressed, presenting an opportunity to understand the scope of forthcoming costs, which can further define matter strategy.

Conversely, engaging experts later in the lifecycle can help avoid immediate costs to some degree, but the tradeoff here is that a later technical read on the matter may reveal missed opportunities to refine and sharpen the litigation strategy and potentially increase overall costs as experts will need to adjust their effort level and attention to deal with the same facts and issues but with less time remaining on the clock. In turn, this also increases the risk that the expert’s analytical approach and efficacy is weakened because the remaining time may prevent them from achieving the same degree of thoroughness.

The most successful outcomes we have seen strike this balance. In those matters, we are engaged reasonably early to perform technical analyses and reach preliminary opinions and conclusions. Those preliminary opinions and conclusions then become discussion points in settlement negotiations, which is a period during which expert witness work is typically reduced while the parties try to resolve the issue. If the matter does not settle, the expert is already familiar with the technical detail and can proceed into deeper analyses, or refine earlier ones, as trial approaches.

For more information about DisputeSoft and its services, please visit www.disputesoft.com, or contact DisputeSoft at https://www.disputesoft.com/contact-us/, or directly via email at inquiries@disputesoft.com, or via phone at 301-251-6313.

The International Legal Finance Association has filed its response to the UK Government’s consultation on competition redress, arguing that reforms intended to speed up the opt-out collective actions regime must not add cost or complexity that makes meritorious claims harder to fund.

The submission responds to the Department for Business and Trade’s consultation on “Swifter and Simpler Competition Redress, Regulatory Appeals and Competition Enforcement,” which opened on 17 July and closed on 25 September. ILFA’s central argument is that the Competition Appeal Tribunal and the appellate courts have already developed workable mechanisms for overseeing class representative suitability, and that the Government should deliver clarity through guidance and the formalisation of existing practice rather than new statutory or procedural requirements.

“Third-party litigation funding is the cornerstone of the opt-out collective actions regime,” said Neil Purslow, Chairman of the Executive Committee of ILFA. “Without it, consumers and small businesses would have no realistic means of bringing meritorious claims against well-resourced defendants. In our response, we make it clear that any new reforms must not inadvertently introduce cost or complexity, which only serve to make valid claims harder to bring.”

ILFA ties the Government’s proposal to permit damages-based agreements in collective proceedings to the unresolved question of funder returns. “Crucially, the Government’s proposal to permit damages-based agreements in collective proceedings underscores the urgent need to reverse the PACCAR ruling retrospectively,” Purslow said. “To keep this regime viable and investable, we must give funders earlier certainty over returns and introduce better cost budgeting to rein in unpredictable, disproportionate costs.”

On costs, the association supports mandatory costs budgeting for claimants and defendants alike from certification onwards, and greater use of alternative dispute resolution where it is required early and backed by real costs sanctions. It also backs a central CAT website for claims and settlements, while cautioning that efficiency measures such as reduced panel composition may yield only marginal savings.

“Maintaining a true equality of arms is essential,” Purslow said. “Large defendants should not be allowed to weaponise structural hurdles to quash meritorious claims and ordinary businesses and consumers must remain empowered to hold the powerful to account.”

Law firms, funders and legal departments are being sold AI for contract review, legal research and citation checking, and the models change every few months. So we looked only at independent studies published in 2026 that tested the current generation of models from OpenAI, Anthropic and Google on real legal tasks. The short version: the best models are now genuinely good at reading and extracting from documents you give them, still unreliable at recalling law from memory, and the commercial legal research tools lag behind the best custom systems.

At a Glance

Best overall model for legal document work: Google's Gemini 3.1 Pro. It was at or near the top in every 2026 study that tested it, and it was usually the fastest and cheapest of the leaders. OpenAI's GPT-5.5 found slightly more errors in contract review, and Anthropic's Claude models were the most careful about not flagging problems that weren't there.

Best accuracy recorded on a full legal task: 92%, on a 50-state statutory research test run by Stanford, achieved by a purpose-built research tool. The lesson is that how the AI is set up matters as much as which model sits underneath it.

Range of accuracy: from under 7% (asking a model to recall exact case citations from memory) to 99–100% (catching a citation to the wrong case when the model can read the source). Most real-world document tasks landed between 60% and 85%.

Westlaw and Lexis AI: 58% and 64% accuracy on a Stanford statutory survey test, below a custom-built tool at 83–92%.

Biggest single improvement: giving the model the actual documents instead of asking it from memory cut fabricated citations from roughly 15–40% to about 4–15%, and to under 0.2% with a well-built retrieval system.

Key Takeaways

The model to use. For contract review and extraction, start with Gemini 3.1 Pro. It matched the top performer on catching contract errors (74% vs. 75%) at about one-seventh of the cost and in 90 seconds instead of nine minutes. If catching every possible issue matters more than time or cost, GPT-5.5 with reasoning turned on found the most. For checking citations in a brief, the best 2026 results came from GPT-5 running as an agent and from Claude Code with Claude Opus, which was the most precise.

How to Use It

• Give it the documents. Never ask a model to supply case law or citations from memory.
• Turn on the model's "reasoning" or "thinking" mode for review work. It improved error-catching by 9 to 11 points in contract review.
• Use it as a first pass and a second reviewer, not the final reviewer.
• For research, use a tool that pulls from a full, current database of the law, because weak retrieval, not the model, causes many of the errors.

What to Expect

• Contract extraction (pulling out dates, parties, termination and liability terms): about 80–84% accuracy for the best models.
• Final contract proofreading (defined terms, cross-references, inconsistent language): the best models catch about three-quarters of errors. On a 60-page agreement, expect it to miss some.
• Citation checking: nearly all citations to the wrong case get caught, but wrong pinpoint pages slip through 20% to 60% of the time.
• Research answers grounded in the right documents: roughly 6–11% of answers still contain an unsupported statement.

What to Look Out For

• Citations from memory. When asked to recall exact citations without sources, even the best model scored under 7 out of 100, and 20 of 21 models gave confident, wrong answers more than 94% of the time.
• Right case, wrong page. Models tend to approve a citation because the case is on the right topic, even when the cited page doesn't support the point.
• Questions with a false premise. If your question assumes something that isn't true, models often go along with it.
• Legal research tools' marketing. Westlaw AI and Lexis+ AI trailed a custom-built tool by 19 to 25 points on a Stanford test.
• Studies funded by vendors. Some of the best-looking results come from companies selling legal AI. Check who ran the test.

Best Practices

• Ground every task in source documents, and require the model to quote the passage it relied on.
• Check every citation yourself at the pinpoint page before filing. Automated checkers help but don't replace this.
• Turn on reasoning mode for review tasks and accept that it's slower.
• Test a model on a few of your own documents before rolling it out. Rankings change by task.
• Re-test when a new model version arrives; this field moves in months, not years.
• Keep a human reviewer accountable for anything that goes to a court, a client or a counterparty.

What the Studies Found

Contract proofreading. In August 2026, researchers had experienced lawyers plant errors in contracts (misused defined terms, wrong cross-references, wrong party names, contradictions) and tested ten current models on catching them. GPT-5.5 caught 75% of errors, Gemini 3.1 Pro 74%, Claude Sonnet 4.6 69% and Claude Opus 4.7 62%. GPT-5.5 cost $1.38 per contract and took about nine minutes; Gemini 3.1 Pro cost $0.19 and took about 90 seconds. Turning on reasoning mode added 9 to 11 points. Every model was far cheaper than a lawyer, and none was close to perfect.

Contract extraction. A May 2026 study tested models on pulling 26 standard fields out of contracts. Among the major models, Gemini 3.1 Pro scored highest (82%), with Claude Opus 4.6 (82%) and Claude Sonnet 4.6 (80%) close behind and GPT-5.4 at 78%. A smaller legal-specific model built by the study's authors scored 84% at far lower cost. The authors work for Onit, which makes that model.

Made-up citations and facts. A January 2026 study had expert reviewers check 2,700 legal answers from 12 models. Asked without source documents, the best models (GPT-5.2 and Gemini 3.0 Pro) cited something false about 15–17% of the time, and the worst over 30%. Giving the models the relevant documents cut that to about 4–15%. A more carefully built retrieval system brought it below 0.2% for every model.

Research with sources. A March 2026 study found that when models answer from retrieved legal texts, Gemini 3.1 Pro produced unsupported statements 5.7% of the time versus 11.3% for GPT-5.2, and that the quality of the search step mattered more than the choice of model. Its authors sell the search component that performed best. An August 2026 study of eight research setups found unsupported answers ranging from under 10% for the best to nearly half for the worst, with the worst results on questions built on a false assumption.

Westlaw and Lexis. In a February 2026 Stanford study, researchers tested legal AI tools against a Department of Labor survey of state unemployment insurance laws. Westlaw AI scored 58% and Lexis+ AI 64%, while a custom statutory research tool scored 83%, rising to 92% after the researchers found that some of its "errors" were gaps in the government's own survey.

Citation checking. A June 2026 study found more than 1,000 court filings containing fabricated citations, a number growing every year, and tested AI checkers on catching them. GPT-5, working as an agent that looks up cases, caught 83% of planted errors; Claude Code running Claude Opus 4.8 was the most precise and scored best overall. No model reliably caught wrong pinpoint cites, partly because page numbers often sit behind Westlaw and Lexis paywalls. A separate August 2026 study found models catch 93–100% of citations to the wrong case but miss many citations to the wrong page, and even GPT-5.4 with full reasoning missed 40% of wrong pinpoints in court opinions.

Citations from memory. A May 2026 study built from 1,000 real U.S. judicial opinions asked 21 models to recall exact case citations without any sources. The best, Claude Sonnet 4.5, scored under 7 out of 100.

The Bottom Line

The 2026 research is consistent: today's best models, led by Gemini 3.1 Pro, GPT-5.5 and Claude, are useful and cheap for first-pass contract review and extraction when they work from the documents in front of them. They still invent law when asked from memory and still miss wrong pinpoint citations, so a lawyer has to verify anything that leaves the building.

Sources (All 2026)

• Bang et al., "ContractScrub: A benchmark for final review of legal contracts" (Aug. 2026), arXiv:2608.20204
• Lincoln et al., "A Few Good Clauses: Comparing LLMs vs Domain-Trained Small Language Models on Structured Contract Extraction" (May 2026), arXiv:2605.05532
• Dantart, "Reliability by design: quantifying and eliminating fabrication risk in LLMs" (Jan. 2026), arXiv:2601.15476
• Butler and Butler, "Legal RAG Bench: an end-to-end benchmark for legal RAG" (Mar. 2026), arXiv:2603.01710
• Das et al., "How Much Do Legal RAG Systems Still Hallucinate?" (Aug. 2026), arXiv:2608.14210
• Afane et al., "Benchmarking Legal RAG: The Promise and Limits of AI Statutory Surveys" (Feb. 2026), arXiv:2603.03300
• Liu, Stammbach and Henderson, "Who Checks the Citations? Benchmarking Legal Hallucination Detection" (June 2026), arXiv:2606.21155
• Verma, "Is this Citation on Point?" (Aug. 2026), arXiv:2608.12571
• Chen et al., "LegalCiteBench: Evaluating Citation Reliability in Legal Language Models" (May 2026), arXiv:2605.10186

South Florida consumer legal funding marketplace ClaimAngel says it has deployed more than $144 million across over 30,000 individual fundings since launching in April 2023, positioning standardised pricing as its answer to the cost criticisms that dog the consumer funding sector.

As reported by Refresh Miami, the platform runs a marketplace in which 27 funding providers compete for cases, with funders reserving a case in an average of 11 seconds. Advances carry 27.8% simple interest with no compounding and a 2x cap on total repayment, and remain non-recourse — plaintiffs owe nothing if the case is lost.

The company reports serving more than 14,500 plaintiffs and over 750 law firms, with 46 employees. A Case Equity product lets plaintiffs draw against expected case value for living expenses while litigation is pending.

ClaimAngel was co-founded by Jeremy Alters, a trial lawyer of more than two decades who was disbarred by the Florida Supreme Court in 2018 for misusing client funds, and his son Logan Alters. “I did things wrong. They were my fault. I take full responsibility for it,” Jeremy Alters said, describing the company as “born out of an ethics issue.” He applied for readmission to the Florida Bar in 2025.

Planned expansions include attorney funding, a secondary marketplace for buying and selling existing positions, and AngelScore, a data-driven underwriting system.