John K. Zaid & Associates scales personal injury practice with EvenUp's proactive AI platform
The problem
As caseloads grew, John K. Zaid & Associates struggled to maintain case velocity while relying on spreadsheets for medical tracking, manual demand drafting that risked missed details, and routine client check-ins that competed with higher-priority case work.
Workflow diagram · grounded in source
1
AI Playbooks intake evaluation
Ai action
Playbooks proactively automate early case evaluation, assessing liability, injury severity, insurance coverage, and overall client fit.
▾ source quote
“Playbooks proactively automate early case evaluation, assessing key factors such as liability, injury severity, insurance coverage, and overall client fit—helping the investigations team quickly determine whether to take on a new case”
2
Risk-based case triage and routing
Routing
High and low-value claim indicators are surfaced proactively so cases can be triaged before attorneys get involved.
▾ source quote
“By proactively surfacing indicators of high and low-value claims and flagging potential red flags early, the team can triage cases more effectively before attorneys get involved. For higher-value matters, the firm can easily automate cases to the appropriate specialists for …”
3
Centralized medical management
Ai action
Medical Management provides a centralized, continuously updated view of each case including medical procedures, treatment patterns, and expenses.
▾ source quote
“EvenUp's Medical Management™ provides a centralized, continuously updated view of each case, including medical procedures, treatment patterns, and expenses”
4
Automated client check-in agent
Ai action
Communication Agents proactively automate routine client touchpoints, surfacing treatment gaps, pauses, and changes in symptoms.
▾ source quote
“Communication Agents™ proactively automate routine touchpoints consistently, allowing case managers to focus on more complex case needs. Across 2,000 cases and 7,500 calls and text messages, the treatment check-in agent surfaced acute problems proactively for Zaid's case management staff.”
5
Issue escalation to case managers
Routing
When issues or additional questions arise, the Agent seamlessly routes clients to case managers via email.
▾ source quote
“When issues or additional questions do arise, the Agent seamlessly routes clients to case managers via email to call the client back”
6
Automated demand preparation
Ai action
Demand creation is streamlined end to end with key case details and medical records surfaced automatically, significantly reducing time-intensive review.
▾ source quote
“proactive automation of demand preparation workflows. Demand creation was streamlined end to end, with key case details and medical records surfaced automatically, significantly reducing the need for time-intensive review”
7
Negotiation sheet and case insights
Output
Case managers generate Negotiation Sheets in AI Drafts to automatically surface key facts, timelines, and medical evidence with in-line citations.
▾ source quote
“Case managers generate Negotiation Sheets in AI Drafts™ to automatically surface key facts, timelines, and medical evidence with in-line citations. Throughout the case lifecycle, Case Companion™, EvenUp's legal AI assistant within the platform, turns thousands of files into actionable, verified …”
Reported outcome
The firm now sends 30% more demands month over month without adding staff, secures settlements that are often 300% higher, and has automated routine client communications across thousands of cases while freeing case managers for higher-impact work.
Reported metrics
Month-over-month demand sends30%
Settlement value on select cases300%
Settlements broadly securedoften 300% higher
Treatment check-in agent scale2,000 cases and 7,500 calls and text messages
Show all 6 reported metrics
month-over-month demand sends30%
settlement value on select cases300%
settlements broadly securedoften 300% higher
treatment check-in agent scale2,000 cases and 7,500 calls and text messages
check-in time savingssignificant time
case capacityhandles more cases with the same team
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The firm now sends 30% more demands month over month without adding staff, secures settlements that are often 300% higher, and has automated routine client communications across thousands of cases while freeing case m…
What tools did this team use?
EvenUp, AI Playbooks, Medical Management, Communication Agents, Demands, Express Demands, AI Drafts, Case Companion.
What results were reported?
Month-over-month demand sends: 30%; Settlement value on select cases: 300%; Settlements broadly secured: often 300% higher; Treatment check-in agent scale: 2,000 cases and 7,500 calls and text messages (source-reported, not independently verified).
How is this legal ops AI workflow structured?
AI Playbooks intake evaluation → Risk-based case triage and routing → Centralized medical management → Automated client check-in agent → Issue escalation to case managers → Automated demand preparation → Negotiation sheet and case insights.
This case is one data point. Whether its pattern fits you depends on your volumes, your stack, and your exception load — that comparison is the step no case study can do for you.