AI in the Courtroom: How Tech is Forging a New Era for Injury Law

SAN FRANCISCO, CA – May 22, 2026 – A Texas-based personal injury firm has slashed its deposition preparation time by an astonishing 96% and now recovers the full insurance policy limits in 90% of its qualifying cases, often without making a single concession. This isn't the result of a massive hiring spree, but a strategic integration of artificial intelligence that is reshaping the economics and outcomes of personal injury law.

KGS Law Group, a high-volume litigation practice, recently announced these transformative results achieved through its partnership with EvenUp, a San Francisco-based technology company. The firm's success story is sending ripples through the legal industry, providing a clear signal that AI is moving beyond a buzzword and becoming a formidable tool for leveling the playing field against deep-pocketed insurance carriers.

The AI-Powered Overhaul of Legal Workflows

For growing personal injury firms, the path to expansion is often fraught with operational bottlenecks. The pressure to manage a high volume of cases can lead to inconsistencies, reliance on generic templates, and a reactive posture that undermines a firm’s ability to secure maximum value for its clients. As KGS Law Group expanded, its leadership recognized that its manual processes were becoming a barrier to achieving consistent, high-quality outcomes.

To solve this, the firm implemented EvenUp’s suite of AI products—including Demands™, Companion™, and Medical Management™—across its entire case lifecycle. This technology automates the historically laborious process of drafting demand letters, the critical documents that outline a case's value to an insurance company. By feeding thousands of pages of medical records, bills, and case files into the platform, the firm can generate comprehensive, data-anchored demand packages in a fraction of the time.

KGS now mandates that all cases with a potential value over $50,000 must have an AI-generated demand letter. This standardized workflow ensures every claim is substantiated with meticulously organized evidence and comparable jury verdict data, creating a stronger foundation for negotiation.

“Before EvenUp, there was always pressure to shave something off just to get cases resolved,” said Abraham Garcia, Managing Partner at KGS Law Group, in a statement. “Now we’re able to support our valuations with data, move faster across cases, and negotiate from a position of strength.”

This data-driven approach is a direct challenge to the traditional negotiation dynamic, where insurance adjusters often hold an informational advantage. The AI platform analyzes a case and benchmarks it against a vast dataset of over 250,000 verified data points, providing a valuation range grounded in jurisdictional precedents, injury types, and treatment histories. This arms attorneys with objective evidence to counter lowball offers and justify their demand for full compensation.

From Manual Grind to Strategic Advantage

The impact of this technology extends far beyond drafting documents. By automating routine work, AI is fundamentally changing the roles of legal professionals. Paralegals who once spent days sifting through medical records to create timelines can now generate them in minutes, freeing them to focus on higher-value tasks like client communication and strategic case support. Attorneys are similarly liberated from the manual grind, allowing them to dedicate more time to litigation strategy, depositions, and trial preparation.

The 96% reduction in deposition prep time reported by KGS is a prime example. The firm’s attorneys use EvenUp’s Companion™ tool to instantly retrieve case facts, medical provider details, and key evidence during active litigation. This capability not only accelerates preparation but also enhances an attorney's agility during negotiations and depositions.

The result is not just efficiency but effectiveness. The claim that KGS now recovers policy limits in 90% of qualifying cases without concessions is particularly significant. It suggests that the work product is so thorough and the valuations so well-supported that insurance companies are more inclined to settle for the maximum amount rather than risk a costly court battle. This trend is echoed across the industry, with other firms using similar AI tools reporting settlement increases of up to 40% and a doubling in their rate of hitting policy limits.

A Crowded Field in Legal AI

EvenUp's success with firms like KGS has not gone unnoticed. Bolstered by a recent $150 million funding round that pushed its valuation to $2 billion, the company is a dominant force in the AI-for-plaintiffs market. However, it operates in an increasingly competitive landscape.

A host of startups, including Supio, Precedent, and ProPlaintiff.ai, are all vying to become the go-to AI platform for personal injury firms. Each offers a slightly different value proposition. Supio positions itself as a comprehensive, end-to-end platform covering everything from intake to verdict. Precedent, meanwhile, competes on speed and price transparency, advertising faster turnaround times and a clear per-demand cost, a potential jab at EvenUp's more opaque enterprise pricing model.

This burgeoning competition is a healthy sign for the legal tech market, driving innovation and providing law firms with more choices. The common thread among these platforms is their specialized focus on personal injury law, allowing them to develop domain-specific AI models trained on highly relevant data—a key advantage over general-purpose AI tools.

Navigating the Ethical Maze

While the benefits of AI in law are compelling, its rapid adoption raises critical ethical and regulatory questions that the legal community is actively grappling with. The American Bar Association (ABA) and state bars have issued guidelines reminding attorneys that their ethical duties of competence and client confidentiality extend to their use of technology.

One of the foremost concerns is data privacy. Attorneys have an unwavering obligation to protect confidential client information. Uploading sensitive medical records and case details to a third-party AI platform requires rigorous vetting of the vendor’s security protocols, including data encryption and compliance certifications. Lawyers must ensure client data is not used to train external AI models and remains secure.

Furthermore, the risk of algorithmic bias is real. If an AI is trained on historical data that contains societal biases, it could perpetuate those biases by, for example, undervaluing claims from certain demographics. Accuracy is another major hurdle. The phenomenon of AI “hallucinations”—where a model confidently presents fabricated information as fact—has already led to lawyers facing sanctions for citing non-existent case law in court filings.

These challenges underscore a crucial point: AI is a tool, not a replacement for professional judgment. The ultimate responsibility for the accuracy of a legal filing and the strategic direction of a case remains squarely with the attorney. Effective and ethical implementation requires robust human oversight, continuous training, and a clear understanding of the technology’s limitations.