Legal document review · Production

EvenUp saves Trombly & Singer 40 hours per month by automating medical record review and demand preparation

The problem

Trombly & Singer's complex personal injury cases required extensive, time-consuming medical record reviews and demand package preparation that burdened internal staff, hindered case throughput, and delayed settlements.

Workflow diagram · grounded in source
1
First-draft request submitted
trigger
“Using EvenUp as a tool to help with the first drafts of medical record review and demand writing”
2
AI medical record processing
ai_action
“EvenUp's AI-powered system processes thousands of pages of medical records with precision, extracting key case details and summarizing critical medical histories in significantly less time”
3
Staff reviews structured summaries
human_review
“Trombly & Singer has streamlined the document review to ensure every critical detail is captured efficiently—now receiving structured, easy-to-navigate summaries that enhance decision-making and case preparation without spending hours on…”
4
Missing docs identification
validation
“The missing document identification feature from The Claims Intelligence Platform™ proactively flags gaps in medical records, preventing costly oversights”
5
Automated demand package output
output
“EvenUp's demand package automation reduces the time required to draft high-quality, persuasive demand letters”
Reported outcome

EvenUp saved Trombly & Singer 40 hours of staff time per month, reduced demand processing time by at least one week per case, and uncovered an average of three missing sets of documents per demand package, enabling the firm to scale caseload without additional hires and generating tens of thousands of dollars in savings.

Reported metrics
Staff time saved per month40 hours per month
Caseload capacityScaled Caseload capacity without needing to bring on additional staff hires
Missing document sets found per demand packageaverage of three
Demand processing time per caseat least one week per case
Show all 5 reported metrics
staff time saved per month40 hours per month
caseload capacityScaled Caseload capacity without needing to bring on additional staff hires
missing document sets found per demand packageaverage of three
demand processing time per caseat least one week per case
savings from avoided staff hirestens of thousands of dollars
Reported stack
EvenUpThe Claims Intelligence Platform™
Source
https://www.evenuplaw.com/customers/trombly-singer-evenup/
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

EvenUp saved Trombly & Singer 40 hours of staff time per month, reduced demand processing time by at least one week per case, and uncovered an average of three missing sets of documents per demand package, enabling th…

What tools did this team use?

EvenUp, The Claims Intelligence Platform™.

What results were reported?

Staff time saved per month: 40 hours per month; Caseload capacity: Scaled Caseload capacity without needing to bring on additional staff hires; Missing document sets found per demand package: average of three; Demand processing time per case: at least one week per case (source-reported, not independently verified).

How is this legal document review AI workflow structured?

First-draft request submitted → AI medical record processing → Staff reviews structured summaries → Missing docs identification → Automated demand package output.

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