Legal document review · Production

Richmond Vona halves demand turnaround time and doubles output using EvenUp's legal AI platform

The problem

As Richmond Vona scaled rapidly, attorneys and staff were bogged down by administrative tasks, variable document formats, and time-consuming workflows, with demand drafting highly dependent on individual attorneys and leading to inconsistencies across documents.

Workflow diagram · grounded in source
1
Q&A tool flags case gaps
ai_action
“The Q&A tool flagged missing bills and records early, surfaced arguments proactively, and gave the team an almost Google-like ability to instantly search their case for key facts”
2
AI Playbooks early case strategy
ai_action
“Whether surfacing early opportunities to pursue policy tenders or avoiding costly oversights in liability and damages, AI Playbooks™ helps ensure no opportunity is missed”
3
Medical chronology generation
ai_action
“By distilling thousands of pages of records into accurate, interactive chronologies, MedChrons helped the team quickly surface critical insights”
4
Standardized demand drafting
ai_action
“Previously, drafting a demand was highly dependent on the individual, which often led to inconsistencies. With EvenUp, the firm gained a reliable, repeatable system”
5
Legal professional review
human_review
“each demand is reviewed by EvenUp's team of legal professionals”
6
Consistent demand delivered
output
“the writing is more consistent. It's professional. It's all there”
Reported outcome

Richmond Vona reduced demand turnaround from 14 days to 7 days, doubled monthly demand output without additional headcount, and achieved a $3 million medical malpractice settlement in which MedChrons played a key role in the mediation.

Reported metrics
Demand turnaround time14 days to 7 days
Target demand turnaround (planned)3-day cycles
Monthly demand outputdoubled
Headcount required for increased outputWithout adding headcount
Show all 5 reported metrics
demand turnaround time14 days to 7 days
target demand turnaround (planned)3-day cycles
monthly demand outputdoubled
headcount required for increased outputWithout adding headcount
medical malpractice case settlement$3 million
Reported stack
EvenUpAI PlaybooksMedChronsQ&A toolAI assistant
Source
https://www.evenuplaw.com/customers/richmond-vona-evenup/
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Frequently asked questions

What did this team achieve with this AI workflow?

Richmond Vona reduced demand turnaround from 14 days to 7 days, doubled monthly demand output without additional headcount, and achieved a $3 million medical malpractice settlement in which MedChrons played a key role…

What tools did this team use?

EvenUp, AI Playbooks, MedChrons, Q&A tool, AI assistant.

What results were reported?

Demand turnaround time: 14 days to 7 days; Target demand turnaround (planned): 3-day cycles; Monthly demand output: doubled; Headcount required for increased output: Without adding headcount (source-reported, not independently verified).

How is this legal document review AI workflow structured?

Q&A tool flags case gaps → AI Playbooks early case strategy → Medical chronology generation → Standardized demand drafting → Legal professional review → Consistent demand delivered.

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