Case valuation used to be art

Case valuation used to be art. Now it's data science. EvenUp's Claims Intelligence Platform is trained on hundreds of thousands of injury cases and millions of medical records, and one firm reported a 300% increase in settlement offers on historically undervalued case types after deploying it.

EvenUp is valued at over $2 billion. Lex Machina's predictive analytics achieve 70-80% accuracy on case outcomes. Darrow identifies 5 million+ potential violations per month for class action sizing. These aren't incremental improvements to the old way of valuing cases — they're replacing attorney intuition with machine learning models trained on more case data than any human will review in a lifetime.

What Case Valuation Actually Requires

Case valuation is the process of estimating what a case is worth — for settlement negotiations, litigation budgeting, intake decisions, and client advising. It's one of the most consequential judgment calls in practice.

For personal injury: what are the damages, what are similar cases settling for, what's the jurisdiction's track record, how strong is liability, and what are the litigation costs likely to be? Getting this wrong in either direction hurts: undervaluation leaves money on the table, overvaluation leads to rejected settlement demands and trial risk.

For litigation analytics: what's the probability of success, what do comparable cases yield, how does this judge rule on similar motions, and what's the opposing counsel's track record? This data exists in court records, but extracting actionable intelligence from millions of filings requires scale that human research can't match.

For class actions: what's the potential class size, what are the per-member damages, what's the expected settlement range, and what are the litigation costs? Sizing a class action wrong means either pursuing unviable cases or undervaluing viable ones.

AI changes valuation from an experience-based estimate to a data-driven prediction. Not perfectly — but systematically better than intuition alone.

Best AI Tools for Case Valuation

EvenUp dominates personal injury case valuation. Their Claims Intelligence Platform, powered by their proprietary Piai model, is trained on hundreds of thousands of injury cases and millions of medical records. It analyzes case facts, medical records, and treatment patterns to generate settlement demand packages with data-backed valuations. Raised $385 million total, valued over $2 billion. John K. Zaid & Associates reported a 300% increase in settlement offers on certain case types. Best for: Personal injury firms that want data-driven demand packages.

Lex Machina (LexisNexis) provides litigation analytics across multiple practice areas with 70-80% accuracy on case outcome predictions. It analyzes the behavior of judges, attorneys, and parties in federal courts to predict outcomes, damages, and settlement ranges. The 2026 Class Action Litigation Report showed filings surging to the highest level in a decade. Best for: Litigation firms that need judicial analytics and outcome prediction across practice areas.

Darrow specializes in class action case identification and sizing. Their algorithms identify 5 million+ potential violations per month and assess scale of harm, legal grounds, class eligibility, and financial impact. With 3,000+ US attorneys on the platform, Darrow provides projected settlement ranges, class sizes, and estimated plaintiff recoveries. Their analysis of Basich v. Microsoft projected a $41M-$61M settlement range. Best for: Plaintiffs' firms evaluating class action opportunities.

Claude is effective for ad hoc case valuation analysis — feeding it case facts and comparable outcomes to generate reasoned valuation assessments. It won't have the proprietary case databases of specialized tools, but it can analyze the data you provide with strong legal reasoning.

The Data-Driven Case Valuation Workflow

For personal injury (EvenUp workflow):

Step 1: Upload case documents — medical records, bills, police reports, liability evidence. EvenUp's AI processes and categorizes everything.

Step 2: The platform analyzes injury patterns, treatment protocols, and case-specific factors against its database of hundreds of thousands of comparable cases.

Step 3: EvenUp generates a demand package with data-backed valuation, including comparable case outcomes, medical analysis, and damages calculations.

Step 4: Attorney reviews the AI-generated demand, adjusts for case-specific factors the model may not capture (client credibility, specific venue considerations), and sends to the insurer.

For litigation strategy (Lex Machina workflow):

Step 1: Input case parameters — practice area, jurisdiction, judge, opposing counsel, key legal issues.

Step 2: Lex Machina returns judicial behavior patterns, opposing counsel's track record, comparable case outcomes, and settlement/verdict distributions.

Step 3: Attorney uses this data to set realistic client expectations, inform settlement negotiations, and make strategic decisions about case investment.

For class action sizing (Darrow workflow):

Step 1: Darrow's algorithms scan for potential violations matching your practice areas.

Step 2: For identified cases, Darrow assesses class size, damages, jurisdiction, and legal grounds.

Step 3: Attorney evaluates Darrow's analysis against their litigation experience and decides whether to pursue.

The Impact on Settlement Negotiations

Data-driven case valuation changes the negotiation dynamic fundamentally. When your demand package includes statistical analysis from hundreds of thousands of comparable cases, it carries different weight than a demand based on attorney experience alone.

EvenUp's impact on personal injury settlements is documented: one firm reported 300% increases in offers on historically undervalued case types — particularly cases that traditionally attracted low initial offers, like chiropractor-only treatment claims. The AI identifies that these cases settle for more than adjusters typically offer, and the data-backed demand makes the case for higher value.

Lex Machina's analytics give litigators a different advantage: knowing what the judge in your case actually does, not just what the law says they should do. If your judge grants summary judgment 40% of the time in employment cases versus the national average of 25%, that data shapes both your litigation strategy and your settlement positioning.

The biggest shift is at intake. Firms using case valuation AI can assess potential case value before investing significant resources. EvenUp helps PI firms identify which cases justify full litigation investment and which should be settled early. Darrow helps class action firms find cases worth pursuing. This data-driven intake means fewer resources wasted on cases that won't deliver returns.

Costs and ROI of AI Case Valuation

EvenUp pricing is case-based — firms pay per demand package generated. The ROI is direct: if EvenUp increases your average settlement by even 10% on a $100,000 case, the platform cost is trivially offset. The 300% improvement reported by one firm on specific case types makes the ROI calculation almost absurd.

Lex Machina is subscription-based through LexisNexis. Pricing varies by firm size and modules accessed. For litigation strategy and case valuation, the platform pays for itself if it improves your win rate or settlement positioning on even a handful of cases annually.

Darrow operates on a partnership model — they bring case opportunities to firms, typically with shared economics on successful outcomes. There's minimal upfront cost; the value exchange is Darrow's case identification capability paired with the firm's litigation execution.

The broader ROI across all these tools: better intake decisions (fewer resources wasted on low-value cases), stronger negotiating positions (data-backed demands), more accurate client advising (realistic expectations from day one), and improved firm profitability through data-driven case portfolio management.

EvenUp has already helped secure billions of dollars in settlements across its platform. At scale, the data advantage compounds — the more cases the model processes, the more accurate its valuations become.

The Bottom Line: EvenUp for personal injury case valuation and demand generation. Lex Machina for litigation analytics and judicial prediction across practice areas. Darrow for class action identification and sizing. The common thread: attorneys who negotiate with data outperform attorneys who negotiate with intuition. These tools don't replace legal judgment — they arm it with evidence that changes outcomes.