AI client acquisition is still a young discipline. Our research program focuses on reproducible law-firm buyer scenarios, recommendation behavior, source patterns and what actually changes when a firm improves its record.
How consumer-facing law firms appear in AI-driven discovery across practice areas, with a focus on the client questions that lead to firm selection.
Research report will be added through the post-launch publishing workflow.A scenario-based look at how PI firms are recommended for accident type, geography, urgency, language and client constraints.
Research report will be added through the post-launch publishing workflow.A practical methodology for law-firm owners who want to build scenarios, test AI, improve evidence and re-run the benchmark themselves.
Research report will be added through the post-launch publishing workflow.Where practical, we want every benchmark report to disclose the scenario logic, measurement window, platform conditions, test frequency and limitations so a sophisticated reader can understand what the numbers do—and do not—mean.
The language gap between legal taxonomy and the way real buyers describe a problem.
Why PI, immigration, family law and criminal defense require different benchmark logic.
How recommendation behavior can differ across engines and why a single-platform test is incomplete.
Public sample scenarios showing how matter, geography, language, urgency and decision stage interact.
What needs to be structured when agents move from recommending a firm to taking the next step on a user’s behalf.
Research improves the realism of the scenarios used in live client benchmarks.
Source analysis helps distinguish changes that matter from changes that merely look technical.
Repeated testing helps us separate persistent movement from normal answer variability.
Start with a free public-data check. If you want a customized scenario benchmark built around the matters you want more of, the $199 baseline is the next step.