Your read on the merits. Our read on the clock.
Push a case's resolution out and the same recovery is a lower annualized return — and the reserve behind it stays locked the whole way. Duration moves IRR, lockup, and reserves directly, yet it's the one input most books still carry without a measured distribution behind it. The Tertius Duration Engine supplies that distribution — trained on 9.1M federal cases, graded in public below against a million more it never saw — so your judgment on the merits runs on a calibrated clock.
The Tertius Duration Engine
No LLM guesses a duration — a duration is too expensive to hallucinate. The Engine is a proprietary survival model, fit by maximum likelihood on millions of real dockets. Four things it does that a spreadsheet, a rule of thumb, or a generic model can't.
Built for litigation's real shape
Cases settle in waves, then grind on for years. A bell curve can't draw that. Our engine fits the one distribution family built for exactly this shape — the same math used to model machine failure and drug survival, tuned for a federal docket.
A different tail for every case type
Prisoner petitions resolve fast and tight. Antitrust drags for years. One global shape would blur both. The Engine fits a separate tail per case type — so your patent case's p90 isn't quietly borrowed from cases that behave nothing like it.
Corrected for the era it's in
Dockets have sped up and slowed down for decades — e-filing, COVID backlogs, rule changes. The Engine fits a continuous filing-year trend, so a case filed today is priced off the current docket regime instead of a thirty-year average. It tracks long-run drift; no model pins a single year's shock.
Never forgets what's already survived
A case alive for two years cannot resolve on day 200 — that outcome is gone. The Engine conditions on time already survived, so an open case gets the forecast for where it actually stands, not the naive one for a case freshly filed — and that conditional path is scored on held-out data at 1, 2, and 3 years elapsed.
Reads the filing, not just the label
Pro se cases resolve ~20% faster; class actions and jury demands each run about a third slower. The Engine conditions on what the complaint already tells you — pro se status, class allegations, jury demand, jurisdiction basis — so two 'contract' cases stop getting the same number.
Knows how cases end, not just when
For every case type: the share that settle, get dismissed, reach judgment, or get swept into an MDL — each path with its own clock, estimated only on cohorts old enough to be fully observed. Procedural priors from 8 million labeled resolutions. Your merits view stays yours; now it has a base rate.
Checked against the government's own numbers
The federal judiciary publishes one timing statistic — median months to disposition, per district (AO Table C-5). We replicated their exact metric from our cleaned data across three separate years and matched their statisticians district-by-district to a fraction of a month, with trial counts agreeing within 2%. Every large gap traced to a measurable cause: mass-MDL waves we exclude by design, so an ordinary case's forecast never inherits 200,000 earplug claims settling at once. The full method, sources, and reproduction script ship in the repo — and everything the government doesn't publish (forecasts, tails, path probabilities, portfolios) is graded on the calibration page instead.
From clock to capital
A duration curve is academic until it's money. The Engine runs a Monte Carlo across every open case's fitted distribution — thousands of simulated paths — and turns it into an IRR fan chart, a capital-lockup curve, and a recycling schedule, with every correlation assumption stated explicitly rather than buried.
Win rate and recovery multiple are your inputs, labeled as yours. We don't sell a merits guess — we sell the clock, priced honestly, so the judgment you already trust is running on a measured number.
Scored on your book
The production model is trained exclusively on public federal court records — no customer data is in it. The outcomes you record do something more useful to you: they grade Tertius against your own realized results, on your own book, out of sample — the accuracy number no vendor benchmark can fake. They also count toward clearly-labeled future recalibration milestones; if a future model version ever trains on customer-contributed outcomes, its calibration page will say so before it ships. Captions, parties, and amounts never cross customer boundaries.
One rule never bends: a resolution date has to appear in the actual docket text, or nothing gets written. Zero invented dates. Scoring only works if the data is clean.
Known limitations
A model you'd underwrite with is a model whose edges are documented. These are ours, stated here rather than discovered in diligence.
Very large demands are a weak regime
The public federal data top-codes amounts demanded at $9.999M, so the training data cannot distinguish a $12M case from a $500M one. The Engine pools everything above $1M into a single bucket — a case with more than $10M at stake gets a '$1M+' forecast, not a distinct >$10M one. If your book lives above that line, backtest it there before relying on the numbers.
Calibration varies by case type
The 80% band is a target, not a guarantee: coverage differs across case types, and some groups run below target. The per-group table above and the calibration page publish every group's coverage with its sample size — check your categories, not the average.
Long-pending cases are the hardest
Forecasts for cases already years into their life are scored separately, at 1, 2, and 3 years elapsed — and coverage declines as elapsed time grows. The cases that have already survived longest are disproportionately the intrinsically slow ones, and the current model only partially captures that selection. Modeling it fully (per-case frailty) is on the model roadmap; until then, the dynamic scores are published so you can see the gap.
Federal district courts only — and docket time, not cash time
Coverage is U.S. federal district court civil cases; state-court matters are flagged on import and not forecast. And the Engine predicts when the district-court docket terminates — settlements can pay on schedules and judgments can be appealed and collected later, so cash receipt can lag the forecast resolution.
Don't trust the scatter plot. Test it on your own book.
Import your last 20 closed cases. The Engine forecasts them blind and shows you, case by case, how close it would have been.
Run your last 20 closed cases through itTertius produces statistical timing estimates — not legal advice, never merits or damages. Federal civil only.