Also from Splitifi: Criterica · Criterica Intelligence — outcome, settlement & duration prediction for institutional capital

Splitifi
BUILT FOR
Litigation FundersLaw FirmsInsurance Carriers
Litigation Finance Intelligence

Quantify legal risk.

Deterministic models for underwriting, portfolio analysis, and recovery optimization — built from real case outcomes across federal and state dockets.

Explore a Partnership
CASE LIFECYCLE · SECURITIES FRAUD · S.D.N.Y.
1
FILED
Day 0
2
DISCOVERY
Month 3–8
3
MEDIATION
Month 12–18
4
RESOLUTION
Month 18–28
MODEL OUTPUT
The Iron Triangle

Every funding decision reduces to three variables.

Win probability. Expected award range. Time to resolution. Splitifi models all three — across every major litigation finance vertical, with jurisdiction and judge specificity that generic legal AI cannot provide.

WIN PROBABILITY
P(favorable outcome)
AWARD RANGE
E[damages + fees]
TIME TO RESOLUTION
E[months to close]
What We Model

Every stage of the funder lifecycle.

Case Selection

Qualify cases against win probability and award thresholds before commitment. Move from instinct-driven deal flow to model-driven pipeline discipline.

Portfolio Risk

Model correlation risk, jurisdiction concentration, and timing exposure across portfolios. Identify concentration before it becomes a problem.

Settlement Timing

Predict when cases will resolve and at what range — by case type, jurisdiction, and judge. IRR modeling requires time estimates. We provide them.

Award Range

Expected value distributions for damages, fees, and interest based on comparable outcomes. Not point estimates. Distributions.

Recovery Probability

Model defendant solvency risk, judgment-proof exposure, and collection probability. Win probability and recovery probability are not the same number.

Vertical Coverage

1,000 production models across 20 LF categories.

VERTICALSTATUSKEY OUTPUT
AntitrustPRODUCTIONDamages, liability probability
Patent / IPPRODUCTIONValidity, infringement, damages
SecuritiesPRODUCTIONClass certification, settlement range
CommercialPRODUCTIONBreach, damages, enforcement
BankruptcyPRODUCTIONRecovery rate, plan confirmation
EmploymentPRODUCTIONLiability, verdict range
Mass TortPRODUCTIONPer-claimant value, aggregate range
Intl. ArbitrationPRODUCTIONAward probability, enforcement
Asset RecoveryPRODUCTIONCollection probability, yield
InsurancePRODUCTIONCoverage, bad faith, damages
Model Scenarios

The three numbers every funding decision reduces to.

Select a vertical. See win probability, award range, time to resolution, and IRR projection — modeled from real outcomes in that vertical.

MODEL REGISTRY
antitrust_liability_damages_v2
VERTICAL
Antitrust
CASE INPUTS
CLAIM TYPE
Price-fixing / Sherman §1
DEFENDANT
Fortune 500 — Consumer goods
DAMAGES THEORY
Overcharge — $340M claimed
CIRCUIT
2nd Circuit — S.D.N.Y.
UNDERWRITING OUTPUTS
WIN PROBABILITY
67%
EXPECTED AWARD RANGE
$180M – $310M
TIME TO RESOLUTION
26 – 38 months
RECOVERY PROBABILITY
94%
IRR PROJECTION

Internal rate of return modeled across three scenarios using win probability × expected award ÷ time-to-resolution.

BASE CASE22%
BULL CASE31%
BEAR CASE11%
Institutional funders typically target IRR ≥ 20% on a risk-adjusted basis. Base case projection uses P50 award × win probability.
Splitifi Platform

Splitifi powers Criterica Intelligence — outcome, settlement, and duration models purpose-built for institutional capital. Regulated market coverage across EPA, SEC, ERISA, antitrust, and civil enforcement. Delivered through Atlas, the institutional portal live today.

Criterica Intelligence →
Integration

Available under NDA for qualified institutional partners.

Access via REST API, direct data feed, or MCP. Structured as a commercial partnership with SLA-backed uptime and dedicated implementation support.

REST API

Structured endpoints per vertical. Standard JSON. Deterministic response.

MCP

AI-native access. Tools for portfolio analysis, case underwriting, and IRR modeling.

Direct Feed

Bulk data delivery for quantitative teams. Parquet or JSON. Structured under data license.