EvenUp

Sweet spot: a plaintiff PI firm with 15–200 active matters at any time, where demand-letter throughput is the actual bottleneck and the managing partner can see paralegal hours-per-case as a P&L line. EvenUp compresses that hours-per-case number dramatically, and the valuation dataset gives the negotiating attorney a defensible number to anchor settlement talks. The 2,000-firm install base means the product has been hardened against the workflow quirks of real PI practice rather than legal-tech demos. Failure modes. Treat every demand letter as a first draft — the medical narrative must be lawyer-reviewed before it leaves the building, both for hallucination risk and for tone calibration. Valuation suggestions are statistical comparables, not predictions of your specific jury — over-relying on the number in negotiation is how firms leave money on the table or under-settle. Multi-state firms should pilot per-jurisdiction; demand quality and valuation depth are not uniform across all 50 states. What to pilot. Pick 20 recently-settled cases where you know the final number and the demand-letter quality cold. Run EvenUp on the same inputs. Compare draft quality, valuation accuracy, and paralegal time saved. If demand quality matches your senior paralegal's output and time-per-case drops 50%+, the seat math works at most firm sizes; below that, the playbook needs tuning before scaling.

EvenUp is a vertical AI platform built specifically for plaintiff personal-injury law firms. The flagship workflow is AI demand-package generation: a paralegal uploads the medical records, bills, and incident documents, EvenUp's system extracts the clinical narrative, builds the chronology, calculates damages, and drafts a full demand letter in the firm's voice — work that historically takes a paralegal 10–20 hours per case. EvenUp also ships intake automation that scores incoming matters for value, treatment-management agents that chase missing records, negotiation tooling that auto-builds case financials, and litigation modules for discovery drafting and trial-prep document generation. The case-valuation engine is grounded in Piai, which EvenUp markets as the largest proprietary personal-injury dataset in the industry — 250,000-plus verdicts and settlements used to benchmark a new claim against comparables by injury type, jurisdiction, and venue. That dataset is the moat: general-purpose legal AI tools do not have access to PI-specific outcome data, so their valuation suggestions are educated guesses where EvenUp's are statistically grounded. The competitive frame is narrow on purpose. Eve.legal is the closest direct competitor — also AI for plaintiff lawyers, also pushing demand generation and case management — but with a broader plaintiff-litigation framing rather than EvenUp's laser focus on personal injury. Generalist legal-AI tools (Harvey, Lexis+ AI, Casetext / CoCounsel) do not solve the PI-specific workflow because demand letters, medical-record chronologies, and settlement valuation are not their training emphasis. Inside the PI vertical, EvenUp is the most-funded and most-deployed option, with 2,000-plus PI firms cited as customers. Pricing is sales-gated and varies by firm size and module set; EvenUp does not publish per-case or per-seat pricing publicly.