Tools that forecast
Project what happens next, with uncertainty attached.
66 of 388 tools.
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 agentic action loss
Forecast expected and tail operational AI-agent loss with tenant-local empirical-Bayes absorbing Markov chains across execution, deviation, containment, recovery, completion and loss, preserving shared control-failure regimes and unresolved-chain mass.
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 capability atrophy loss
Learn how aggregate fallback capability decays with AI reliance and is preserved by unaided practice using a Bayesian right-censored transition model, then forecast ready/degraded/unavailable capacity and correlated provider-outage economic VaR/CVaR.
Forecast AI code maintenance liability
Forecast the long-run maintenance liability of aggregate AI-assisted code inventory with a Bayesian Gamma-Poisson recurrent-event model, learned AI/complexity/age hazards, lognormal severity and correlated repository shock VaR/CVaR.
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 evaluation production validity
Forecast whether offline AI evaluation scores will remain valid in production using a tenant-pooled Bayesian logit calibration with workload effects and time drift, coherent operating scenarios, false-promotion risk, breach timing, net value and quality-shortfall 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 output IP claim liability
Forecast aggregate AI-output IP claim frequency, gross cost, defense and disruption, collectible indemnity and net VaR/CVaR using tenant-local Bayesian recurrence/severity evidence plus shared provider events and counterparty default.
Forecast AI privacy attack loss
Forecast correlated membership-inference or reconstruction loss from tenant-local member/nonmember red-team trials using Beta posterior attack advantage, partially pooled lognormal harm, binomial subject exposure, coherent attacker regimes, common asset-group compromise, control effects and loss VaR/CVaR.
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 route quality cost drift
Forecast route-level quality, inference cost, p95 latency, breach timing, net value and economic-loss VaR/CVaR with partially pooled Bayesian trends and one common disruption state shared across every route on the same provider.
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 alert fatigue and missed risk loss
Forecast alert storms, duplicate notifications, aggregate attention-state saturation, missed material conditions, interruption cost and financial VaR/CVaR with a Markov-modulated Gamma-Poisson and compound log-normal model.
Forecast analytics calibration survival
Forecast how long each locally calibrated analytical function remains decision-safe using right-censored calibration episodes, a discrete empirical-Bayes failure hazard, conditional survival from current calibration age, posterior uncertainty and explicit endpoint-support gates.
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 change adoption bass diffusion
Forecast aggregate organizational change or tool adoption with a Bayesian Bass diffusion model learned from reconciled historical cohorts, jointly estimating spontaneous innovation and imitation, simulating posterior uptake under per-cohort enablement capacity, pricing enabled value, exposing grid-boundary misspecification, and gating a target adoption probability.
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 engineering investment benefit realization
Forecast whether an engineering-investment portfolio will realize finance-defined benefits within a decision horizon using a partially pooled Bayesian hurdle/lognormal model for zero-benefit risk, positive benefit multiples, and realization lag; correlated organization shocks; discounting; NPV/ROI gates; and explicit unseen-category fallback.
Forecast executive technology commitment credibility
Recalibrate executive technology commitments with class-local isotonic Beta posteriors, then simulate correlated on-time outcomes, conditional lognormal delay, value erosion and financial-shortfall VaR/CVaR.
Forecast feature adoption revenue
Forecast feature adoption, revenue, and contribution with a grouped discrete-time hazard model trained on reconciled censored cohorts, required to beat a pooled-hazard baseline on later cohorts before posterior and capacity-constrained forecasts are decision-safe.
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 governed attrition competing risks
Forecast voluntary departure, internal transfer and involuntary exit as calibrated discrete-time competing risks with company-local chronological validation, peer partial pooling, posterior intervals and an automatic abstention when the model does not beat role base rates.
Forecast governed release competing risks
Forecast company-local rollback, hotfix and incident incidence conditional on deployment with inverse-propensity-corrected discrete-time competing risks, strict whole-release chronological validation, posterior intervals and mandatory improvement over both a simple baseline and the legacy PR score.
Forecast growth channel response saturation
Learn organization-specific channel saturation from resolved aggregate incrementality estimates: fit a likelihood-weighted Bayesian grid of Hill response curves, reserve the newest periods for honest validation against a linear baseline, expose posterior boundary misspecification and evidence failures, and return contribution and marginal-return distributions for proposed spend levels.
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 intervention effect half life
Learn how quickly a governed intervention's effect decays across resolved cohorts using a shared exponential half-life, cohort-specific amplitudes, a persistent floor, reported standard errors, and a profiled Bayesian grid; then forecast effect/value paths and when each current intervention is likely to fall below a practical threshold.
Forecast joint engineering outcome distribution
Learn a company-local partially pooled discrete Bayesian network from complete mature observations, validate it strictly out of time against an independent baseline, and answer coherent conditional joint engineering-outcome queries with exact inference and Dirichlet posterior intervals.
Forecast knowledge continuity semimarkov
Forecast critical code-knowledge continuity with a company-local hierarchical Bayesian semi-Markov model whose state-exit hazard depends on time already resilient, concentrated, orphaned or recovering; require a strict latest-period holdout improvement over persistence, simulate coherent common shocks, and expose orphaning, delay, recovery-cost and portfolio VaR/CVaR without predicting named departures.
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 organizational change second order effects
Forecast the incremental capacity, review, knowledge, quality, backlog, recovery and financial distribution of a submitted departure, hire, restructure, reassignment, PTO, AI rollout, framework migration or contractor scenario using company-local completed episodes and a dependency DAG.
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 recurring revenue regimes
Fit a diagonal-Gaussian hidden Markov model to consecutive organic ARR growth, GRR, gross margin and cash-burn intensity, order latent regimes by growth rather than arbitrary labels, test effective regime support and improvement over a single-state model, then simulate ARR and unrestricted cash through fitted transitions, within-regime variation and coherent common market scenarios.
Forecast shadow AI unseen exposure loss
Estimate AI services missed by every detector with a Bayesian zero-truncated binomial capture–recapture model, then simulate scenario-dependent visibility, incidents, common provider exposure, usage, value disruption and lognormal loss to produce unseen-inventory and economic VaR/CVaR tails.
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.
Forecast workflow absorption semimarkov
Forecast terminal workflow outcomes and remaining time from Bayesian transition and lognormal dwell-time posteriors over status histories.
Simulate contextual thompson bandit
Simulate Bayesian contextual Thompson sampling and quantify intervention reward, regret, and policy uncertainty.
Simulate delivery flow digital twin
Simulate delivery as a network of finite queues with stochastic arrivals, lognormal service, rework, WIP limits, and policy economics.
Simulate dependency cascade risk
Stress-test correlated baseline failures and directed dependency cascades with portfolio loss VaR/CVaR and risk contributions.
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.
Stress test causal effect robustness
Quantify the omitted-confounder partial-R² strength required to erase a causal point estimate or its statistical significance, benchmarked against observed covariates.
Stress test investment memo assumptions
Stress an investment memo's local value model by shrinking weak claims toward declared adverse values, pricing pairwise nonlinear interactions, and finding the first failure fraction along a joint adverse path.
Stress test knowledge resilience
Simulate partial-mastery knowledge coverage under individual and correlated team shocks, attribute continuity criticality, and optimize cross-training under money and capacity constraints.
Stress test operating plan assumptions
Stress every operating-plan assumption individually and along a common adverse path, exposing remaining outcome headroom and the linear breakpoint at which the plan fails.