Commercial Silk Dismantles Expert Witness Testimony Using Trialview AI

In complex litigation, manual case preparation is now too slow and ineffective for the huge amounts of evidence law firms are faced with before going to trial. This case study demonstrates how a commercial Silk effectively leveraged TrialView's AI to prepare for the cross examination of an expert witness by transforming a huge volume of documentation into a decisive tactical advantage.

The Challenge

In recent, high-stakes construction litigation, Emily Egan McGrath SC, a commercial silk with over 20 years of experience, found herself confronted with a huge volume of complex, highly technical evidence.

The opposing party’s case relied heavily on expert reports that claimed that widespread structural damage was caused by the use of defective materials across a large number of properties, and they alleged that the pattern of the damage across other developments supported their conclusions. The expert reports contained generalised statements that were not linked to specific properties, and appended approximately 135,000 pages of inspection and sampling reports, laboratory reports and building condition assessments - all contained in the trial bundles. 

With between 10 -12 lever-arch folders of exhibits per expert, manually sifting through the data to review the accuracy of these statements was not only inefficient but impossible to achieve in a short amount of time.

The Solution

To help prepare for the hearing, Ms. Egan McGrath, with the assistance of the team from Arthur Cox, her instructing solicitors,  deployed TrialView’s platform.

Using TrialView’s AI functionality, she executed granular queries that cut through the detail of the technical reports and surfaced crucial information that could be used as part of her case strategy. 

For example, to disprove the generalised statements contained in the expert reports, she was able to interrogate her case portfolio with TrialView’s AI search tools. Using targeted searches across tens of thousands of pages, she was able to surface examples of damage patterns, chemical data and test results that undermined key aspects of the  expert witnesses’ statements. AI searches across a large volume of detailed chemical and petrographic reports and surveys were able to identify specific results and documents that could be relied upon to demonstrate that the expert report overstated the nature of the damage and did not present a fair and complete summary of the chemical and petrographic results.  

Crucially, this also ensured effective cross-referencing. Using this information, counsel was able to systematically dismantle the expert’s credibility.

Ms. Egan McGrath noted that, without the AI tool, identifying these critical discrepancies would have required at least three months of manual review. Whereas by using TrialView, the team were able to form their case strategy and create bundles in a matter of weeks. Additionally, due to TrialView’s automatic bundling features, late additions were included with ease, with the platform automatically updating the cross-referencing, page numbers and hyperlinks. 
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"I found the AI search function fantastic for surfacing information," Emily noted. "Where initial review of the underlying reports was carried out to identify target and specific search terms, TrialView would surface all of the information that is connected to a particular strategic query with 100% accuracy and you can get incredibly detailed with your searches. Plus all of the related links and documents are always shown alongside your searches so that you can review the relevant documents, add them to your cross-examination bundle, and put them to the witness during cross-examination.”

The ability to upload documents to private bundles on TrialView also proved invaluable in preparing to cross-examine expert witnesses. For example, Ms. Egan McGrath requested that her solicitors, with the assistance of her client’s  own experts, search for previous publications by the opposing expert. The relevant articles and book extracts were then uploaded to a private bundle with assigned TrialView numbers, allowing Ms. Egan McGrath to review and select specific extracts to put to the expert during the cross-examination.

The Impact

The ability to instantly surface this critical data changed the entire approach to the trial.  Confronted with the specific laboratory reports she had uncovered, on the stand, the expert witness was forced to accept that a number of the generalised statements contained in their expert report were incomplete and, in certain instances, incorrect or unsubstantiated.

For Ms. Egan McGrath, the experience confirmed that modern litigation requires more than just digitised files. It requires tools that provide a competitive edge to your legal strategy. Reflecting on the necessity of this technology at the Bar, she offers this advice to her colleagues:

"I think a lot of legal professionals don't realise the true depth of what AI tools can actually do for them. It is not just about document storage - it's about gaining a distinct tactical advantage. I would fully recommend TrialView for anyone preparing for a plenary trial. Its AI capabilities are specialised and very impressive, and I will certainly be using TrialView for any of my cases going forward.”

She noted that other market alternatives often struggled to upload large document volumes and were prone to hallucinations or inaccuracies. Whereas TrialView has no limit to its upload capabilities and can effectively search the entire portfolio to identify crucial information. She also emphasised that TrialView’s ability to operate within a closed, already-uploaded evidentiary set provided superior reliability and clarity compared to those other platforms.