What is EvenUp?
EvenUp is a San Francisco legal technology company founded in 2019 for personal injury firms. Its platform analyzes medical records, prepares demand materials, summarizes bills, and flags case tasks. EvenUp sells through custom agreements rather than public self-serve plans. Its per-case model covers platform usage, while order forms define included usage, renewal terms, and overage rates. Firms must request a quote.
Founded in 2019 by Rami Karabibar, Raymond Mieszaniec, and Saam Mashhad, EvenUp develops software for plaintiff-side personal injury practices. The company announced a $150 million Series E in October 2025, bringing reported funding to $385 million and its valuation above $2 billion. EvenUp says more than 2,000 firms use its platform, including 20 percent of the Top 100 United States personal injury firms. It also says customers have resolved more than 200,000 cases representing over $10 billion in damages. The company remains headquartered in San Francisco. Its focused design suits personal injury teams handling substantial medical documentation, but it offers less value to general commercial practices seeking broad legal research, transactional drafting, or unrelated case-management workflows.
A matter can begin with medical records, bills, correspondence, and other documents uploaded to the platform. Piai extracts treatment details, organizes provider information into a timeline, and identifies liability, damages, and missing material. Teams can create demand letters, medical summaries, complaints, negotiation sheets, interrogatory responses, and related drafts. Express Demands can assemble exhibits and retain line-level references. Firms can choose AI-generated work or a professional review option involving EvenUp legal professionals. Smart Workflows flags treatment gaps, missing documents, and timing issues. Case Financials and Medical Bill Summary help staff examine charges against policy limits. Attorneys remain responsible for verifying facts, revising documents, and making legal decisions before using any output.
Piai is EvenUp's proprietary group of personal injury models. The company says the system was developed using more than 100,000 cases and one million medical records and visits, with guidance from more than 150 legal and medical professionals. EvenUp describes three main stages: extracting information from scans and notes, reconciling details across providers, and producing case-grounded work tied to source material. Announcements during 2025 included the AI Drafts Suite, Smart Workflows, Medical Bill Summary, and consolidated case-based pricing. In October 2025, the company announced Mirror Mode, which adapts a firm's preferred draft style, structure, and wording across document workflows. The available material does not verify a later 2026 product release.
EvenUp does not publish a dollar entry price, free plan, or public trial. Its May 2025 announcement described one per-case price covering the Claims Intelligence Platform, replacing separate feature tiers for drafting and workflow work. Public subscription terms state that fees appear in an order form, are paid in advance unless the order provides otherwise, and are generally non-cancelable and non-refundable. Included usage and overage rates also depend on that order form. Additional charges may apply when usage exceeds stated limits. The terms allow annual renewals and fee changes at renewal with notice. This structure lets firms budget around matters, but buyers cannot calculate an exact monthly cost without a sales conversation. Professional review and larger usage commitments may change the quoted amount.
EvenUp's main drawback is commercial opacity. The company describes its products and case-based model, but does not publish plan prices, public usage quotas, a self-serve API, or a standard trial. Eve is a close personal injury competitor with intake, case evaluation, drafting, and litigation workflows. Supio focuses on medical-record analysis, chronologies, and plaintiff case documents. DigitalOwl concentrates on structured medical-record review rather than a complete demand workflow. EvenUp is a strong fit for a personal injury firm seeking medical analysis, demands, financial review, and task alerts from one vendor. It is less suitable for buyers requiring published rates, broad legal research, or autonomous work without attorney checking.
Who is EvenUp best for?
- Personal injury firms processing large medical records and recurring demands.
- Teams wanting drafting, financial review, and workflow alerts together.
- Firms willing to purchase through a sales-led, per-case agreement.
- Practices seeking optional review from EvenUp legal professionals.
EvenUp Pricing (2026)
Pricing model: Paid. All prices in USD.
| Plan | Price | What you get |
|---|---|---|
| Claims Intelligence Platform | Custom | Per-case pricing is available; order forms define included usage, subscription fees, renewals, and overage rates. |
| Express Demands, AI + You | Custom | Piai prepares a demand for firm review and editing; public pricing and case quotas are not published. |
| Express Demands, AI + Professional Review | Custom | Piai prepares the demand and EvenUp legal professionals review it; pricing and volume are quoted. |
Pros and Cons
Pros
- Piai is designed for personal injury records, treatment timelines, and damages.
- Express Demands can preserve line-level references and organize exhibits.
- Smart Workflows identifies missing documents and treatment gaps.
- The platform offers optional professional review for demands.
Cons
- EvenUp publishes no dollar prices, free plan, or public quotas.
- Outputs require attorney review and case-specific factual checking.
- Its personal injury focus limits value in other practice areas.
Who should look elsewhere?
- Small firms seeking a transparent self-serve subscription.
- General commercial practices needing broad legal research.
- Teams unwilling to review AI-generated legal work.
Technical Details
Frequently Asked Questions
How much does EvenUp cost?
EvenUp does not publish a dollar price. Its 2025 announcement introduced an all-in-one per-case model, while subscription terms place fees, included usage, and overage rates in an order form. The quote can vary with services, matter volume, professional review, and usage commitments. Prospective customers must request pricing rather than select a public monthly plan.
Does EvenUp have a free plan or trial?
EvenUp does not publish a free tier or public trial. The platform is sold through custom subscription terms, with fees generally paid in advance and usage limits recorded in the order form. Firms should ask sales about pilots, ramp periods, cancellation rights, included cases, page limits, and any charges that apply after contracted usage.
How does EvenUp compare with Eve?
Both serve plaintiff-side and personal injury practices. EvenUp emphasizes medical-record intelligence, demand packages, case financials, and workflow automation. Eve covers intake, case evaluation, medical review, drafting, and litigation tasks across a broader plaintiff workflow. Buyers should compare integrations, source references, review procedures, supported matter types, implementation requirements, and negotiated pricing.
Does EvenUp train models on customer case files?
EvenUp says customer case files are not used by third-party providers such as OpenAI or Anthropic to train their models, and files do not appear in another customer's work product. Its Trust Center describes statistical product improvement, while Piai materials describe case-level separation and zero-retention settings. Firms should still assess privilege, confidentiality, retention, and contract terms.
Can law firms use EvenUp commercially?
Yes. EvenUp sells to law firms for commercial personal injury work, including case analysis, document drafting, and workflow support. The platform is designed for lawyer supervision rather than autonomous legal practice. Firms should confirm permitted uses, user counts, data-processing obligations, professional-review scope, and billing terms in their order form. Attorneys should review output before relying on it.