EvenUp

What EvenUp does

EvenUp is a legal AI platform built specifically for plaintiff-side personal-injury firms. It analyzes case files, medical records, bills, and other evidence to help teams prepare demand packages, medical chronologies, case summaries, and litigation work product. That specialization matters: a generic assistant may summarize a PDF, while EvenUp is designed around the sequence from intake through pre-litigation and litigation, including injuries, treatment timelines, damages, defense arguments, and case value signals.

The platform's narrow focus is its main differentiation. Search results and catalog evidence describe EvenUp's proprietary Piai model as trained for personal-injury work, with a Drafts suite for demands, chronologies, and workups. Buyers should still compare it with broader legal platforms such as Harvey AI and CoCounsel. Firms building a custom medical-record pipeline can examine Google Document AI, while document-centric knowledge workflows may benefit from Docugami. Those alternatives differ substantially: EvenUp packages a PI workflow, whereas general legal AI and document APIs require more configuration.

Pricing cannot be verified publicly

Both EvenUp vendor requests made on September 3, 2026 were blocked by a web application firewall and returned no usable product or price table. Search coverage consistently describes case-based or firm-specific pricing, but no exact amount was reliable enough to publish. The correct price here is contact vendor / manual verification required. Ask for a written quote that separates platform access, per-case or per-document charges, page limits, rush delivery, revisions, integrations, onboarding, storage, support, and unused-volume terms. Model cost per settled case, not merely cost per generated demand. A low document fee can still be expensive if attorneys spend hours correcting the output.

Benefits, drawbacks, and professional risk

EvenUp can reduce manual review of long medical records and standardize demand-package structure across a growing firm. It is particularly useful when paralegals repeatedly build timelines, extract treatment facts, total bills, or assemble exhibits. Structured workflows can also make case handoffs more consistent. However, legal and medical facts are high impact. A wrong provider, diagnosis, treatment gap, date, amount, policy limit, or causal statement can weaken a claim. AI output may sound polished while overlooking contradictory records or jurisdiction-specific requirements. Pricing opacity makes forecasting difficult, and the specialist product is not intended as a general research replacement. No first-party MCP support was verified.

Pilot with 50 closed matters whose outcomes and attorney-approved work product are known. Include minor injuries, surgery, pre-existing conditions, treatment gaps, disputed causation, multiple providers, liens, handwritten scans, and duplicated records. Measure material factual errors per case, missed bills, chronology precision and recall, attorney editing minutes, turnaround time, exhibit accuracy, and total cost per accepted package. Blind the reviewer to whether a draft is AI-assisted where practical.

Before live use, define who approves every factual assertion, preserve links to source pages, prevent unsupported damages language, and log every revision. Confirm confidentiality, privilege handling, retention, model-training terms, breach notification, data location, subcontractors, and deletion. Prohibit automated filing or sending demands without attorney approval. EvenUp may deliver real value for a high-volume PI practice, but its value is measured by fewer review hours and accurate work product; not by how quickly it produces a polished first draft.