Virginia Injury Law scales caseload and achieves 69% YoY revenue growth with EvenUp's Claims Intelligence Platform
As Virginia Injury Law grew, maintaining consistency across every case became harder. The firm faced a demand generation bottleneck and inconsistent, time-consuming processes that prevented it from scaling without sacrificing quality.
Before EvenUp, missing records and bills were not identified until the demand generation stage, causing significant back-and-forth between teams and delays in settling cases.
With the same team size, Virginia Injury Law achieved a 69% year-over-year revenue increase, reduced case review time from 30 to 45 days to 1–2 hours, and saw faster case resolutions, higher average settlements, and stronger client satisfaction.
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Frequently asked questions
What did this team achieve with this AI workflow?
With the same team size, Virginia Injury Law achieved a 69% year-over-year revenue increase, reduced case review time from 30 to 45 days to 1–2 hours, and saw faster case resolutions, higher average settlements, and s…
What tools did this team use?
EvenUp, Claims Intelligence Platform, Piai, Case Companion, Executive Analytics, Case Preparation, MedChrons.
What results were reported?
Volume of demands sent: increase in the volume of demands sent from 2023 to 2024; Year-over-year revenue growth (headline teaser): year-over-year growth in revenue; Time on case analysis reduction (headline teaser): reduction in time spent on case analysis using Case Companion; Year-over-year revenue increase: 69% (source-reported, not independently verified).
What failed first in this deployment?
Before EvenUp, missing records and bills were not identified until the demand generation stage, causing significant back-and-forth between teams and delays in settling cases.
How is this legal document review AI workflow structured?
AI demand letter generation → AI case fact summaries → Medical records gap detection → Policy-limit case routing → Leadership KPI analytics.