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AI-native legal intelligence

Price legal riskbefore it prices you.

Calibrated probability and expected-value bands for litigation capital—with confidence, provenance, and the risk drivers behind every number.

Run a shadow pilot Explore the engine
Confidence-aware Provenance-first API-ready
UNDERWRITING / MATTER 00482 LIVE
Commercial dispute · SDNY

Doe v. Acme Corporation

Calibrated confidence
87%
High evidence density
Projected damages
$1.85M $3.42M
ConservativeUpper band
Settlement probability67%
Key risk driversImpact
Venue history+8.4%
Judge disposition+4.1%
Comparable matters12 found
Missing discovery−6.8%
Illustrative underwriting output

Built for the decisions behind litigation capital

PROBABILITYEXPECTED VALUEEXPOSUREPROVENANCECALIBRATION
01 / THE GAP
The opaque world of legal risk

Capital is committed before risk is measured.

Funders and insurers commit millions on intuition and inconsistent diligence—with no calibrated view of downside exposure.

In a world of data-driven finance, legal risk remains a black box. Veredict turns subjective conviction into an auditable underwriting decision.

THE REARVIEW MIRROR

Traditional legal analytics

  • Retrospective case research
  • Point estimates and binary guesses
  • Confidence hidden from decision makers
THE UNDERWRITING LAYER

Veredict intelligence

  • Calibrated EV and exposure bands
  • Explainable drivers and provenance
  • Portfolio-ready decision support
02 / THE ENGINE
From raw data to predictive power

Evidence in.
Calibrated risk out.

Structured legal events feed a prediction layer benchmarked against baselines and human experts. Every outcome sharpens the next decision.

01

Structure the matter

Dockets, pleadings, judge history, venue, posture, damages, and comparable cases become a decision-ready legal-event graph.

02

Calibrate the risk

Backtested models produce probability, expected-value, timing, and downside-exposure bands—never unsupported point estimates.

03

Trace every signal

Every output carries provenance, confidence, and missing-data flags so investment committees can audit the reasoning.

FEDERAL DOCKETS
LEGAL-EVENT GRAPH
CALIBRATION LAYER
AUDITABLE OUTPUT
03 / THE FLYWHEEL
The compounding advantage

Every matter makes the system harder to copy.

A labeled legal-event graph, calibration history, and private outcome feedback loop compound into a proprietary prediction ensemble.

  1. 01Labeled legal-event graph
  2. 02Calibrated prediction layer
  3. 03Private funder feedback loop
  4. 04Enterprise data flywheel
  5. 05Proprietary ensemble
“
One saved bad bet pays for a decade of software.

Decision-grade intelligence for funders, insurers, and legal-risk teams.

04 / WHY NOW
The quant shift in law

The underwriting layer for legal capital is being built now.

01

AI maturity

Advanced models and ensemble techniques make defensible legal-risk forecasting possible.

02

Data maturity

Dense federal dockets now support structured, backtest-calibrated analytics.

03

Market pressure

Capital providers need transparent, quantified exposure—not another research database.

04

Regulatory demand

Provenance and explainability are becoming requirements in regulated decision systems.

For litigation funders and legal-risk insurers

See what your underwriting process is missing.

Request a shadow pilot

Bring one matter. Compare the signal.