Forecasting & survival
Estimate when something completes or fails, with censoring and unresolved work handled honestly rather than dropped.
67 of 388 tools.
Attribute commercial dependency tail loss
Calculate expected loss, VaR and CVaR for commercial value concentrated in shared technical components, then allocate every modeled tail-loss dollar exactly once across components with normalized negative-log survival hazard rather than overlapping leave-one-out sensitivities.
Audit AI evaluation contamination integrity
Audit frozen AI evaluation suites for temporal or answer leakage, model-version mismatch, incomplete pre-label predictions, weak label provenance, missing subgroup support, cross-suite case reuse and near-duplicate content components before evaluation scores are trusted.
Audit AI model routing evidence integrity
Audit every live AI-model route against current version-matched local evaluation, uncontaminated temporal holdout, pricing freshness, residency, retention, reliability and genuinely independent provider fallback evidence, counting each workload's value at risk once.
Audit analytics transportability
Audit whether locally calibrated analytical functions retain decision-loss improvement across target-similar operating environments using similarity-weighted random-effects meta-analysis, between-environment variance, I-squared, sign consistency and a conservative target prediction interval.
Audit attrition risk prediction integrity
Audit an attrition model's complete eligible cohort, point-in-time features, supportive-use governance, intervention-contaminated labels, competing outcomes, calibration, false positives and authorized subgroup error before any person-level use.
Audit forecast ensemble lineage integrity
Audit whether a claimed forecast ensemble is a complete, independently sealed and point-in-time evidence set rather than duplicated consensus, then score only mature uncontaminated outcomes.
Audit fundraising pipeline integrity
Audit a fundraising pipeline as point-in-time evidence rather than CRM theater: reconstruct monotone stage events, terminal status and primary proceeds, retain open opportunities as censored, reject forecasts made after resolution, detect duplicate active investor accounts, and gate the portfolio on mature-forecast support, Brier loss and calibration gap.
Audit policy feedback performativity
Audit whether deploying a probability-driven policy is associated with a changed score-to-outcome relationship: compute cluster-level exposed-versus-comparison pre/post differences in predictions, outcomes, calibration residuals, and Brier loss; bootstrap the assignment unit; and abstain when baseline balance or score overlap cannot support the comparison.
Audit probabilistic forecasts
Audit whether resolved probability forecasts are accurate, calibrated, discriminating, and better than a base-rate prediction.
Audit release risk prediction integrity
Audit a complete eligible-change release-risk cohort for point-in-time prediction lineage, exact change-to-deployment linkage, mature mutually exclusive outcomes, selective labels, score-triggered intervention contamination, calibration and false alarms before the score influences a release decision.
Calculate break even delivery date
Find the latest economically supported delivery period across coherent value, remaining-cost, recurring-contribution, operating-cost, value-decay, cost-growth and delay-cost scenarios; enforce expected NPV, positive-NPV probability and CVaR gates while keeping the economic deadline distinct from a completion forecast.
Calculate opportunity cost of WIP
Quantify the expected value-delay cost of the current WIP completion pattern against the Smith-rule focus sequence, including scenario probability and tail disadvantage.
Calibrate business case forecasts
Calibrate positive business-case forecasts with chronological empirical-Bayes log-ratio correction, sparse-category shrinkage, proper-score validation, and interval-coverage gates.
Compute robust operating viability kernel
Compute the maximal robust controlled-invariant set of safe operating states under complete set-valued state-action transitions, identify every feedback action that keeps all modeled successors viable indefinitely, and expose finite guaranteed-survival layers for states outside the kernel without using probabilities or rewards.
Conformalize prediction intervals
Apply finite-sample split-conformal inflation to model intervals, with Mondrian group corrections and explicit global fallback for sparse groups.
Estimate delivery delay value at risk
Translate aligned portfolio completion-date draws into expected delay loss, VaR/CVaR, dependence amplification, and initiative tail attribution.
Estimate estimate at completion distribution
Turn bottom-up component actuals and locally calibrated remaining-cost p50/p90 estimates into a correlated Gaussian-copula lognormal estimate-at-completion distribution with antithetic simulation, budget-breach probability, CVaR, correlation uplift, finite-draw error, and exactly reconciled component tail contributions.
Estimate multilevel metric generalizability
Decompose aggregate management-metric variance into unit, period, and residual components, bootstrap reliability, and calculate the sampling needed for dependable comparisons.
Estimate software reliability growth
Estimate long-run software reliability growth with a power-law nonhomogeneous Poisson process, bootstrap trend evidence, and future incident exposure.
Forecast acquisition technology integration economics
Forecast acquisition-technology integration time, cost, stranded cost, synergy realization and economic-shortfall CVaR from pooled lognormal history, dependency paths, finite capacity and shared disruption states.
Forecast aggregate workforce capacity risk
Forecast aggregate role-capacity shortfall with a partially pooled discrete-time competing-risk model that learns cause-specific hazards from right-censored employment spells, simulates the active role portfolio, exposes unseen-role and unsupported-period extrapolation, and never produces named-person attrition scores.
Forecast AI configuration regression loss
Forecast material AI configuration regression, rollback-capped request exposure, excess failures, net value and economic-loss VaR/CVaR from tenant-local concurrent control/candidate evidence, partially pooled lognormal severity, coherent operating scenarios and a shared platform-regression state.
Forecast AI data rights liability
Forecast correlated AI data-rights loss, disruption and response cost with locally calibrated Beta defect priors, partially shared Gaussian-copula occurrence and severity, lognormal harm, coherent jurisdiction scenarios, controls and portfolio VaR/CVaR.
Forecast AI inference avoidable cost
Forecast AI inference spend and the safely avoidable portion from semantic response caching, retry prevention and batching using tenant-local empirical-Bayes rates, log-normal unit demand, shared scenarios, Shapley savings attribution and cost VaR/CVaR.
Forecast AI inference economics
Forecast full AI-inference cost, retry demand, terminal-failure loss, gross value and economic-loss VaR/CVaR with tenant-local Gamma-Poisson, Beta-Binomial and partially pooled lognormal models plus shared provider-outage scenarios.
Forecast AI knowledge staleness loss
Forecast stale and unsupported AI answers plus economic-loss VaR/CVaR by learning tenant-local knowledge-change hazards, retrieval failure and lognormal stale-loss severity, then simulating scheduled refreshes under coherent demand/change/loss scenarios and a shared index-failure state.
Forecast AI regulatory change liability
Forecast counsel-defined regulatory-change frequency, lognormal remediation work and cost, capacity queues, shared jurisdiction shocks, enforcement exposure and liability CVaR.
Forecast AI workflow execution economics
Forecast multi-step AI workflow demand, retry and loop depth, success, p95 latency, full cost, failure loss and net business value with tenant-local empirical Bayes, log-normal attempt economics, shared operating scenarios and common-control failure VaR/CVaR.
Forecast cash burn uncertainty
Forecast aligned cash paths into reserve-breach probability by period, ending-cash uncertainty, first breach timing, and rescue capital required to restore the governed minimum reserve.
Forecast CI feedback loop economics
Forecast company-local CI feedback delay, compute spend, terminal failure and governed post-release escape loss with hierarchical Dirichlet/Beta outcomes, log-normal feedback, runner-queue amplification, coherent common shocks and strict latest-period validation against global baselines.
Forecast cloud cost commitment exposure
Forecast cloud commitment waste, uncovered on-demand cost, savings distribution, probability of negative savings, and CVaR loss over aligned demand paths.
Forecast contract delivery and liability
Forecast remaining commercial-commitment delivery time, on-time probability, contractual penalties, acceptance cash and liquidity from a right-censored empirical-Bayes lognormal duration model, conditioning each live promise on its age and refusing sparse or unverified classes.
Forecast correlated milestone slippage
Forecast joint portfolio milestone slippage from complete historical episode-by-category planned/actual duration ratios: fit log-error marginals and a positive-definite shrinkage Gaussian copula, mix shared and idiosyncratic shocks, propagate durations through the current dependency DAG, and report joint confidence, finish distributions, and tail value at risk.
Forecast cross border data restriction loss
Forecast migration, operating, contract and common jurisdiction loss from counsel-defined cross-border data restrictions with a Gamma-Poisson event model, pooled log-normal duration/cost and coherent tail scenarios.
Forecast customer facing service interruption loss
Forecast customer-facing outage frequency, duration, SLA credits, interrupted revenue, churn exposure and total financial VaR/CVaR using local zero-inclusive service history, compound log-normal severity and coherent shared-dependency events.
Forecast customer lifetime value uncertainty
Forecast prospective customer lifetime value by jointly propagating beta-binomial retention uncertainty and lognormal contribution-margin parameter uncertainty through discounted cohort economics.
Forecast cyber control failure loss
Forecast expected and tail cyber loss with locally pooled threat frequency, control reliability and lognormal loss severity, drawing one shared control state across every path it protects to preserve common-mode failure.
Forecast delivery to cash conversion
Forecast how delivery-ready, accepted and invoiced value converts to collected cash and minimum liquidity from complete right-censored stage episodes, empirical-Bayes cohort/age hazards and coherent shared scenarios, while refusing unsupported stages or unverified evidence.
Forecast dependency adjusted consensus
Combine independently sealed human and model forecasts while learning context base rates and source reliability on earlier questions, discounting empirical and declared information dependence, and abstaining unless later questions beat both base rates and naive consensus.
Forecast focus fragmentation delivery economics
Forecast current-task completion and delay-cost tails with a company-local ridge log-normal accelerated-failure-time model that uses attention covariates only after beating a global model on the latest whole period.
Forecast fundraising close and runway
Forecast whether enough primary capital closes before runway pressure by fitting empirical-Bayes age-state competing-risk hazards to advanced, closed, lost and right-censored stage episodes, then simulating every live opportunity under one common market scenario and an explicit burn-before-close cash convention.
Forecast hiring ramp capacity
Forecast an aggregate hiring plan with a locally calibrated hierarchical lognormal ramp-time model, Weibull productivity curves, correlated organization shocks, mentor-load displacement, commitment risk, and discounted capacity economics.
Forecast incident learning debt economics
Forecast how much corrective-action debt will remain open and what recurrent incident and operating loss it may create using hierarchical closure, recurrence and severity models that must beat global baselines on the latest whole period.
Forecast infrastructure cost elasticity
Select a continuous piecewise log-log infrastructure cost response on an internal future block, refit before an untouched chronological holdout, validate against constant unit cost and interval coverage, then forecast price-index-restored cost across coherent workload scenarios.
Forecast onboarding time to autonomy economics
Forecast remaining time-to-autonomy and delay-cost tails with a company-local right-censored, partially pooled log-normal AFT model that must beat a global baseline on the latest whole cohort.
Forecast operational recovery half life
Forecast how quickly operational performance recovers after incidents, migrations, reorganizations, outages, or other shocks: estimate each resolved shock's exponential remaining-loss half-life, retain stalled trajectories at a governed cap, partially pool log half-lives by severity, and simulate current recovery confidence plus cumulative value loss.
Forecast org health operating loss
Prove whether the company-local organization-health score leads later delivery, reliability and capacity losses, then simulate their correlated economic tail only after a latest-whole-period baseline challenge passes.
Forecast organizational change load capacity
Forecast whether the organization's planned portfolio of migrations, launches, reorganizations, policy changes, and platform transitions exceeds aggregate operating capacity: select a saturating distributed-lag change-load model on pretest history, beat an autoregressive baseline on later periods, then simulate peak strain and limit-breach probability.
Forecast privileged identity exposure loss
Forecast aggregate privileged-identity compromise frequency and financial tail loss with separate Gamma-Poisson security states, locally pooled log-normal severity and one coherently simulated common identity-provider event.
Forecast receivables collection and liquidity
Forecast cash collection, disputes, defaults and minimum liquidity from right-censored receivable histories: fit empirical-Bayes categorical transition probabilities by lawful aggregate risk class, state and age; retain censored exposure; simulate every current aggregate receivable under shared market/cash scenarios; and abstain on unsupported states, unverified evidence or inadequate liquidity probability.
Forecast software supply chain loss
Forecast aggregate software supply-chain loss with tenant-calibrated empirical-Bayes Gamma-Poisson incident frequencies, partially pooled lognormal loss marks, common frequency/severity/business scenarios, and multiplicative unique-application disruption paths; unsupported risk classes make the output diagnostic-only.
Forecast support cost to serve
Forecast future support cost and budget-breach probability with a chronological held-out lognormal regression on accounts, supported products, and ticket load.
Forecast technical asset obsolescence
Forecast product retirement, technical obsolescence, security/compliance retirement and vendor/platform end as competing technical-asset risks using complete right-censored lifecycle episodes, age-specific empirical-Bayes Dirichlet hazards, coherent common scenarios and current-age simulation of stranded carrying value plus foregone contribution.
Forecast vendor spend at risk
Forecast correlated vendor spend with lognormal marginals, a Gaussian copula, contractual floors and caps, budget-overrun uncertainty, total-spend CVaR, and reconciled vendor tail contributions.
Optimize focus coordination policy portfolio
Choose aggregate async, meeting-batching, protected-focus or coordination policies using prospectively identified effects shrunk by design reliability, common scenarios, unique shared loss, Pareto search and hard budget, capacity, response, focus, timezone and CVaR gates.
Optimize forecast elicitation portfolio
Choose which independent human or model forecasts to obtain next by learning chronologically validated contextual directional skill, simulating conservative entropy reduction, pricing decision relevance and removing duplicated information under budget and source-capacity constraints.
Optimize onboarding mentorship portfolio
Choose an interference-aware mentorship portfolio using only prospective controlled effects, reliability shrinkage, common autonomy scenarios, unique shared loss, mentor capacity, service gates, CVaR and exact-or-disclosed beam search.
Optimize reliability investment frontier
Construct a dependency- and exclusion-feasible Pareto frontier across investment cost, expected residual loss, CVaR loss, and expected downtime under coherent scenarios and explicit pair interactions, then select the least-cost evaluated portfolio clearing governed reliability targets.
Optimize shadow AI governance portfolio
Choose block, broker, migrate, allow-with-controls or monitor policy for each aggregate shadow-AI service class, maximizing expected value minus loss CVaR under residual-exposure, detection, control, dependency, exclusion, budget and resource gates while pricing common provider value at risk once through joint survival.
Rank features by evidence adjusted ROI
Rank feature investments by reliability-shrunk ROI, downside probability, and CVaR using aligned outcome scenarios and an explicit skeptical prior.
Rank initiatives evidence adjusted value
Rank initiatives using an explicit mixture of finance-approved value scenarios and a skeptical prior weighted by backtested evidence reliability, with downside and CVaR gates.
Reconcile hierarchical delivery forecasts mint
Reconcile independently produced portfolio, product, team, repository, or workstream forecasts into one additive hierarchy using shrinkage MinT: learn the cross-level residual covariance on training forecasts, prove coherence, gate accuracy on later untouched periods, and return coherent current forecasts with uncertainty intervals.
Score investor execution vitals
Give startup investors an evidence-shrunk execution signal spanning milestones, runway, reliability, and resilience.
Simulate startup financing survival
Simulate dependency-gated milestone execution, correlated fundraising conditions, event-timed burn and insolvency to quantify survival, financing dependence, and rescue capital.
Stack resolved probability forecasts
Fit convex weights to frozen probability forecasts on chronological training history and require bootstrap-validated log-loss improvement over the training-selected best component on future outcomes.
Validate temporal leading indicators
Validate aggregate leading indicators only when their lagged history improves expanding-window forecasts beyond target autoregression, with block inference and FDR.
Value dependency unblocking
Value shortening one blocker by propagating aligned duration scenarios through a dependency DAG, repricing earlier completion under task-specific value decay, and mixing unblock success or failure after cost.