The asymmetry problem.

There is a structural information gap at the center of construction-defect and bad-faith litigation. The defense side has priced exposure from aggregated data for decades. The plaintiff side has worked from experience and instinct. That gap shapes nearly every negotiation, and almost no one talks about it directly.

The defense side’s data infrastructure

Insurance carriers and large defense firms are among the most sophisticated data consumers in the legal market. They maintain actuarial models trained on millions of claims outcomes; they subscribe to verdict databases, loss-run repositories, and specialized analytics services. The carrier seated across the table from a plaintiff attorney knows — with statistical confidence — how similar claims have settled, which venues skew one way or another, and where the exposure envelope sits for this type of matter.

This infrastructure is not a recent development. Verisk Analytics, the data and analytics company that underpins much of the insurance industry’s risk-pricing function, serves carriers and insurers globally. ISO ClaimSearch has tracked claim histories across the industry for decades. Defense-side litigation analytics is a mature and well-capitalized market.

The plaintiff bar, by contrast, has largely operated without comparable data infrastructure. Individual practices build mental models from the matters they’ve handled — which is valuable, but covers a tiny slice of the market. An attorney who has handled fifty construction-defect cases in a given county has a feel for that market. That is not the same as a systematic read on the behavior of every major builder operating in that jurisdiction over the past decade.

Why the gap persists

The information asymmetry is not accidental. It reflects structural differences in how each side of the bar relates to data.

Carriers repeat. A single carrier handles thousands of similar claims per year. That volume creates the feedback loop that makes data valuable: enough claims to train a model, enough outcomes to test a hypothesis, enough repetition to surface real patterns. A plaintiff attorney handling a hundred matters over several years is dealing with a fundamentally different data environment — high-stakes, low-repetition, high-variance.

Defense economics also support investment. A major carrier can spend meaningfully on analytics infrastructure and recoup the cost across the entire book of business. A plaintiff-side practice, even a productive one, is scaling an investment across a much smaller denominator.

And the information itself is fragmented. Public records — permit filings, licensing records, regulatory notices, court dockets — are scattered across dozens of agencies, databases, and jurisdictions. Assembling a coherent picture of builder behavior, or carrier conduct, from those sources is an engineering problem, not just a research problem. It requires the kind of data infrastructure that most plaintiff practices have no reason to build in-house.

What the data-driven alternative looks like

The shift happening in plaintiff-side litigation analytics is not about replacing attorney judgment. Experienced litigators have read on posture, leverage, and timing that no dataset can fully replicate. The goal is to add a layer underneath that judgment: aggregate, market-level intelligence that grounds intuition in something more than the handful of matters any one practice has happened to see.

What that looks like in practice:

• Before a demand, knowing how this builder has responded to similar claims in aggregate — not just in one or two matters, but across the market.
• Before a bad-faith claim, knowing this carrier’s propensity profile: how they handle claims in this line, in this jurisdiction, relative to the market.
• Before a settlement discussion, knowing where the aggregate benchmarks sit — not just what you’ve seen, but what the market has produced.

That information already exists in the public record. Civil Remedy Notices filed under Florida’s §624.155 document carrier conduct across tens of thousands of claims. Permit and licensing records document builder activity across every jurisdiction. Court dockets and case filings accumulate the outcomes. The data is there. The challenge is assembling and interpreting it at a scale that makes it useful.

The market is beginning to shift

The litigation analytics market on the plaintiff side has been slow to develop, but it is developing. Tools like Darrow AI and Rain Intelligence — both venture-backed and focused on plaintiff-side case identification — have demonstrated that investors and attorneys see value in data-driven approaches to plaintiff litigation. Lex Machina and Docket Alarm have made court-data analytics accessible to both sides. VerdictSearch aggregates jury verdict data that plaintiff attorneys can use for benchmarking.

What’s less developed is the specific intelligence layer that matters most for construction-defect and bad-faith practice: the aggregate read on defendant and carrier behavior in the regulated data that those industries generate. The permit records. The licensing complaints. The Civil Remedy Notices. The IFCA filings. These datasets exist, but they require substantial data engineering to turn into market intelligence.

That is the gap DAIS Analytics is built to close.

What this means for plaintiff attorneys

The attorney who enters a negotiation with a data-grounded read on the other side is not playing a different game. They are playing the same game with a more complete hand. The decisions — whether to push, when to settle, where the exposure envelope sits — remain judgment calls. But judgment grounded in market intelligence is better than judgment grounded in instinct alone.

For construction-defect and bad-faith practice specifically, the public-record data ecosystem is unusually rich. Florida’s regulatory framework generates filings that, in aggregate, reveal patterns in builder and carrier behavior that are otherwise invisible at the level of any individual matter. Washington’s IFCA notice process creates a similar public record on the carrier side. That data is aggregate, anonymized, and legally obtained — and it is largely untapped.

Closing the asymmetry does not require carriers to share their models. It requires assembling the public record, applying the analysis, and making the output decision-ready. That is what DAIS Analytics is built to do.