legal_document_review · services · workflow
Richmond Vona halves demand turnaround time and doubles output using EvenUp's legal AI platform
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.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Q&A tool flags case gaps
EvenUp's Q&A tool flagged missing bills and records early and surfaced arguments proactively.
Tools used
EvenUpAI PlaybooksMedChronsQ&A toolAI assistant
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.
Results
Time saved14 days to 7 days
Cost replaced$3 million
Grounding & classification
Source type: vendor customer story
30 fields verified against source quotes.
agent assistdocument aiknowledge searchsummarizationknowledge basemedical recordfailure mode describedhuman review describedmetric backednamed customerproduction runtime claimedtools describedworkflow describedlegalcycle time reductionemployee productivitythroughput increasevendor customer storylegal document reviewlegal opsai draft human approvaldocument to record