Why ‘Go to Trial’ AI Must Be Accurate

By Stephen Dowling, CEO, TrialView.

Even the best-intentioned disputes lawyer will, at some point, encounter what might be called ‘superhero syndrome’; that instinct to win the case, and to win it personally. It is a seductive fantasy, and not an entirely unhealthy one, but it can also present as a distraction.

The reality is that success in litigation is built on more prosaic foundations, won through process without which courtroom flair counts for nothing. The legal team that gets to grips with the material early and co-ordinates their resources to find and marshal the truly important evidence is more likely to succeed at trial.

Now, with AI, the methods have changed. But the principle remains the same. For AI to be effective in litigation it needs to be built on a system that understands the rigorous process that leads to success at trial. Without this it will likely be inaccurate. This is worse than useless. It is dangerous.

Effective ‘Go to Trial’ AI is about process driven accuracy, across vast data sets that accurately and reliably surfaces the relevant material that will lead to success. The best AI platforms create an eco-system that allows the smartest legal minds to get the best out of the latest models and provides a system of reinforced verification that understands litigation workflows thereby ensuring robust case preparation.

The Story So Far

For decades, technology in document-heavy litigation was primarily about simple storage and retrieval. The question was more about the best way to hold large volumes of material in an accessible, searchable form. This was good but it had significant limitations. A keyword search will find documents containing a particular term. It will not find documents that contain adjacent terms or sit within a chain of communications whose significance only becomes apparent when read in context. In other words, traditional search was not built for nuance.

This is precisely where AI adds genuine value. Modern AI tools are capable of understanding context rather than merely matching terms, can work through document sets at a scale and speed no human review team can match. More importantly, they can surface material that a keyword-based approach would miss entirely. In document-intensive litigation, we are not talking marginal gains. The findings can be determinative, and the implications for trial preparation are significant.

Existing Limitations on AI Models

However, even the best AI models today have an in-built limitation. The context window. They can only comprehend a limited number of pages at a time. Approximately 1,000 to 2,000 pages depending on which model you use. Most litigation data sets go well beyond this number. And this is where most AI errors and hallucinations begin.

So first and foremost a litigation tool needs an AI system to be built and processed in a way that can handle this. This involves using AI alongside a deep understanding of litigation data. The best AI litigation tools can both organise the material in a structured way and use that very structure to unearth the critical material.

What makes TrialView particularly well-suited to complex cases is its Case Intelligence functionality. Where a conventional AI tool stops at analysing a document or a limited series of documents, TrialView goes a lot further. It’s case intelligence organises tens of thousands of documents into well defined categories. Pleadings, witness statements, depositions, exhibits, disclosure. It can recognise document types based on the nature of the case – construction litigation, commercial disputes, insolvency, negligence – and it can analyse the data based on a deep understanding of how litigation unfolds in cases of this kind.

Its Case Intelligence functionality is built on a pipeline that processes and crawls an entire data set and gives the legal team a command of the entire corpus of evidence, surfacing connections across the material and supporting the development of case theory. It functions, in practice, as a subject matter expert and an always-available case assistant that stands ready to serve up the critical information the legal team needs.

Accuracy in AI – The Number One Priority

The result is a remarkable accuracy when it comes to retrieval. Tens of thousands of pages can be interrogated with incredible sophistication even across a one-shot query.

With a simple prompt, complete and accurate chronologies can be constructed across vast datasets; patterns of communication identified and tested; and inconsistencies between witness accounts and contemporaneous documents surfaced systematically. The factual preparation underpinning a case can be more thorough, and the risk of late disclosure or unexpected documentary evidence more effectively managed.

TrialView’s Case Intelligence functionality has demonstrated something very few other platforms can show. Across data sets of tens of thousands of pages, it can verify that the information being retrieved is 99% accurate and 99% complete having regard to the underlying documentation. TrialView’s internal benchmarking is demonstrating what appears to be unparalleled accuracy in the litigation space.

TrialView has demonstrated what becomes possible when this level of accuracy is reached. In a complex fraud case that played out in the Commercial Court in London, Quinn Emanuel faced a particularly challenging dataset. Tens of thousands of documents containing allegations spanning eight years, with evidence comprising old emails, handwritten notes, spreadsheets, and complex trading data accumulated over a period that began more than fifteen years ago. The documents were difficult to navigate and resistant to conventional review. Using TrialView’s AI,  the team was able to cut through to the material that mattered, surfacing extracts that directly contradicted a number of the claimants’ central assertions. The tool delivered unmatched accuracy across tens of thousands of pages, not in a controlled environment, but under the demands of live litigation.

Case Management Transformed

The integration of AI with live case management systems is already underway with TrialView. TrialView’s Case Intelligence feature can track the development of a case in real time: tracking events, actors and issues as newly relevant documents are added, flagging inconsistencies as witness statements are served, and updating issue analysis as the litigation evolves. The static document review exercise evolves into a dynamic, continuous process.

There is also serious development underway in AI’s capacity to analyse legal argument itself,  to assess the strength of a position against relevant case law, identify weaknesses in a chain of reasoning, and model how a court might respond to a particular submission. The direction is clear. A gradual shift in the role of AI from a tool that handles volume to one that contributes to legal analysis.

The Future is Defensible AI in Disputes

The profession’s standards around competence, candour to the court, and the integrity of legal reasoning do not change because the tools do. If anything, they become more pertinent as the technology becomes more capable and its outputs more persuasive.

Practitioners cannot get any benefits from AI unless the outputs are verifiably accurate and defensible throughout. Many of the efficiency gains are otherwise lost.

TrialView is among the few AI platforms building directly towards the vision of defensible AI within advocacy. Its ‘Go-to-Trial’ offering is designed to support the legal team across the full arc of a case, from early case assessment through to the hearing itself. Rather than addressing a single stage of the litigation process, it operates as a continuous intelligent presence, organising and interrogating the material as the case develops, and keeping the team across the evidence when it matters most. For practitioners managing complex, document-heavy disputes, the value of that kind of end-to-end support is considerable.

But most importantly, it is a system designed to assure advocates and litigation professionals that the time they save surfacing critical information can be safely spent crafting their arguments rather than checking their AI generated outputs.

To learn more about TrialView please see here.

About the Author: Stephen Dowling is the CEO and Founder of TrialView, an award-winning AI platform designed to streamline dispute resolution processes. Stephen is also a practicing Senior Counsel in Ireland, with over 20 years’ of experience in commercial and civil litigation.

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[ This is a sponsored thought leadership article by TrialView for Artificial Lawyer. ]