AI tools for team leads
The tools most relevant to team leads. These are pure functions — they compute from the arguments passed in and never read your tenant data — so unlike GitRevio's skills they are not persona-gated. Persona here is a relevance hint, not an access control.
366 of 388 tools.
Aggregate risk register copula
Aggregate risk-register occurrence and lognormal severity marginals through a validated Gaussian copula into expected loss, VaR, CVaR, dependence amplification, and tail shares.
Allocate attention budget
Use exact knapsack optimization to allocate limited expert-review time by expected avoided loss.
Allocate budget with CVAR constraint
Maximize expected portfolio return while keeping probability-weighted loss CVaR below a finance-owned tail-risk ceiling across aligned joint scenarios.
Allocate capacity by marginal value
Allocate indivisible aggregate capacity across initiative-specific diminishing marginal-value scenario curves, activation thresholds, hard minimum commitments, unit cost, and portfolio CVaR with discrete next-unit value and explicit solver certainty.
Allocate capacity nash bargaining
Allocate discrete shared capacity by weighted Nash social welfare over concave team utility curves, with disagreement guarantees and a utilitarian counterfactual.
Allocate restless bandit interventions
Allocate scarce recurring interventions across evolving Markov units with Whittle indices, explicit indexability checks, and paired policy simulation.
Analyze coordination entropy
Quantify privacy-safe cross-team seam complexity, concentration, latency, and failure load.
Analyze deep uncertainty minimax regret
Apply Savage minimax regret when scenario probabilities are not defensible, compare maximin and equal-weight choices, and use PRIM-style iterative peeling to discover compact context boxes where the robust choice remains vulnerable.
Analyze delayed management feedback stability
Stress the dynamic stability of a delayed signed organizational feedback model: build a VAR companion matrix from interval-valued lagged influences, evaluate midpoint, interval corners, and sampled simultaneous coefficients, calculate spectral and transient amplification margins, and rank one-edge damping leverage without claiming an exhaustive robust-control certificate.
Analyze info gap robust satisficing
Select a robust-satisficing action under severe uncertainty with Info-Gap Decision Theory: evaluate worst and best payoff across a governed nested uncertainty envelope, maximize the radius before a critical requirement fails, report windfall opportuneness, and use no scenario probabilities.
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 agentic action control integrity
Audit operational AI-agent actions from bounded least-privilege permission scope through independently tested authorization, approval, sandbox, monitoring, rollback or compensation, and kill-switch controls, counting unique value exposure once.
Audit aggregate metric reversal
Detect Simpson's-paradox-style sign reversals between an executive aggregate relationship and its weighted within-stratum fixed-effect relationship, with whole-stratum bootstrap uncertainty and practical-magnitude gates.
Audit AI capability fallback integrity
Prove that every aggregate capability required when AI is unavailable has a current approved runbook and a sufficiently large, timely, successful, independently observed exercise conducted with AI actually disabled.
Audit AI code change evidence integrity
Prove that aggregate AI-assisted coding evidence comes from prospectively registered, nonoverlapping treatment/control studies with immutable assignment, configuration, trace and mature-outcome denominators before anyone estimates an effect.
Audit AI configuration release integrity
Audit that the exact immutable AI configuration bundle evaluated and approved is the bundle exposed in every staged rollout, with consecutive parent lineage, complete blast-radius declaration, effective runtime controls, monotone traffic and a tested prior-version rollback path.
Audit AI data rights provenance integrity
Audit every AI training, fine-tuning, retrieval, evaluation, logging and persisted-output use against an immutable rights grant and the complete derivative lineage, including time, revocation, deletion, purpose, jurisdiction, consent, derivative and evidence gates.
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 inference cost allocation integrity
Reconcile provider AI invoices bottom-up to workload and route usage, price terms, cached requests, retries, fixed charges and credits without combining currencies or silently allocating unexplained spend.
Audit AI knowledge grounding integrity
Audit the complete AI knowledge supply chain from immutable source versions through indexed chunks and effective access policy to retrieved evidence, claim-level citations and honestly mature grounding outcomes, without treating unresolved answers as failures.
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 AI output IP provenance integrity
Audit aggregate AI outputs against the exact model and provider terms effective at generation, pre-commercialization similarity evidence, counsel-owned ownership/use rules, human review and any claimed indemnity coverage.
Audit AI privacy budget integrity
Recompute each aggregate AI privacy account from its immutable release ledger using additive Rényi differential-privacy composition and target-delta conversion, while auditing order grids, sequence, hashes, accounting periods, purpose, review approval, evidence and claimed-versus-actual budget spend.
Audit AI regulatory obligation evidence integrity
Audit point-in-time AI-system classification, counsel-supplied obligation applicability, control evidence and incident-reporting clocks without pretending to infer law.
Audit AI routing experiment integrity
Audit prospectively registered AI-route experiments at the randomization-unit/period/route grain, reconciling logged propensities, allocation fidelity, pre-assignment balance, crossover, outcome maturity, simultaneous-experiment overlap and unique value at risk before any causal effect is reported.
Audit AI workflow trace value integrity
Audit every AI workflow execution from root trace through model, tool, cache, review and control steps to one mature business outcome, reconciling parent lineage, retries, wall-clock latency, direct cost and uniquely attributed net value while retaining unfinished work.
Audit analytical specification multiverse
Audit whether an analytical conclusion survives a prespecified multiverse of admissible windows, cohorts, metrics, and models using aligned bootstrap draws, a weighted specification curve, practical-effect support gates, and descriptive choice-influence diagnostics.
Audit analytics challenger independence
Audit whether an analytical challenger supplies genuinely independent error information: use paired temporal moving-block bootstrap bounds on error correlation, incumbent-failure catch rate and common-mode joint failure, with simultaneous Bonferroni control across every screened challenger and explicit evidence gates.
Audit analytics function calibration readiness
Gate analytical functions on paired out-of-time decision loss against a frozen baseline using temporal moving-block bootstrap, autocorrelation- and weight-adjusted effective sample size, evidence coverage, lower confidence bounds, improvement probability and recent degradation rather than declaring a model calibrated from training fit.
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 attention fragmentation evidence integrity
Audit consented point-in-time contributor identity, availability, privacy-safe calendar metadata and work-session lineage before reporting aggregate meeting load, protected focus blocks or cross-project switching.
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 benefit double counting
Reconcile business-case benefit claims to unique economic source pools and allocation fractions, exposing overallocated sources and claim-level mismatches before portfolio value is aggregated.
Audit budget constraint binding
Audit whether a claimed budget constraint genuinely blocks value after dependency-feasible portfolio reallocation, separating current-plan inefficiency from scarcity with scenario CVaR and a discrete budget shadow price.
Audit cash flow timing consistency
Audit whether economic-event and cash-settlement timing obey governed lag rules across coherent scenarios, quantify the resulting NPV distortion, reconstruct scenario liquidity paths, and separate timing exceptions from liquidity-tail exposure without treating exceptions as wrongdoing.
Audit causal claim negative controls
Gate a causal effect claim using prespecified negative outcome/exposure controls, Benjamini-Hochberg multiplicity control, and an omnibus chi-square falsification test.
Audit CI pipeline evidence integrity
Audit the complete point-in-time change-to-pipeline-to-job-to-rerun cohort, exposing missing CI, orphan records, future leakage, inconsistent required-job outcomes, incomplete provider evidence and same-configuration fail-then-pass flake proxies without scoring people.
Audit cluster randomization integrity
Audit cluster-randomized experiments for practical baseline imbalance and differential outcome observation, with cluster-size-weighted standardized differences and assignment permutation diagnostics.
Audit code knowledge concentration integrity
Audit file, module, service, or repository knowledge concentration from point-in-time substantive changes, reviews, incident response and documentation using identity-confidence filtering, recency decay, Bayesian ownership uncertainty, entropy-effective owners, HHI and leave-top-owner-out resilience—without turning contribution evidence into a person-performance score.
Audit commercial resilience claim integrity
Audit resilience ROI claims against a unique commercial-source to technical-component graph: recompute each action's avoided loss under joint failure scenarios, cap support at graph-derived value, detect duplicate effects, probability drift and weak evidence, and prevent overlapping component benefits from being sold twice.
Audit commercial technical commitment integrity
Audit signed commercial promises against explicitly allocated technical scope, dependency order, funded capacity, acceptance criteria and evidence; expose orphan scope, double allocation, cycles, late plans and maximum contractual penalty without interpreting legal rights from engineering activity.
Audit cost allocation consistency
Audit whether shared engineering, platform, cloud, vendor, or operating cost pools reconcile to source totals and follow their declared pro-rata allocation bases at every target.
Audit cost capitalization sensitivity
Audit whether permitted software-cost capitalization choices change reported project ROI and priority even though scenario cash NPV, downside, and economic rank are unchanged.
Audit cyber control evidence integrity
Audit whether claimed defense in depth is supported by current independent control tests mapped to declared attack-path steps, while preserving duplicate mappings and counting each exposed business asset only once.
Audit data sovereignty residency evidence integrity
Audit every governed data asset's point-in-time storage, processing, replica, backup, log/cache and key locations plus cross-region transfers against an effective counsel-supplied residency policy, independent evidence, encryption controls and retention limits.
Audit decision execution fidelity
Audit whether approved decisions actually became verified implementation at the promised aggregate-unit and component grain, with whole-unit bootstrap uncertainty and simultaneous gates for fidelity, overdue scope, unverifiable evidence, exceptions and critical gaps.
Audit decision flow integrity
Audit management decision histories for unresolved work, state cycles, unowned dwell and excessive lead-time tails using immutable event sequences, whole-decision bootstrap uncertainty, simultaneous flow-level gates and state bottleneck diagnostics.
Audit decision rank robustness smaa
Measure rank acceptability, regret, pairwise dominance, and central winning weights under uncertain criterion scores and bounded stakeholder weights.
Audit delivery to cash chain integrity
Reconcile each governed milestone from delivery-ready evidence through customer acceptance, billing eligibility, net invoicing and collected cash; enforce temporal ordering, eligible-unbilled and outstanding-receivable identities, evidence separation and bounded diagnostics without treating Git activity as an accounting fact.
Audit executive technology reporting integrity
Audit a frozen executive technology pack for complete metric/risk scope, point-in-time source and definition lineage, numerical reconciliation, supported narrative direction, independent review and evidence coverage.
Audit extreme metric tail dependence
Detect extreme metric co-exceedances beyond independence with empirical tail coefficients, permutation inference, practical magnitude gates, and FDR control.
Audit financing term sheet integrity
Audit startup financing terms as exact share, price, proceeds and ownership identities: include pre-money option-pool increases and converting instruments in the pricing denominator, keep secondary purchases out of company cash and post-money share creation, reconcile primary issuance, post-money equity value and reported investor ownership, and retain evidence failures and impossible fees or secondary sales.
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 human AI decision complementarity
Audit whether a governed human-AI decision process reduces prospective loss below the better standalone human or AI policy using paired shadow decisions, cluster bootstrap uncertainty, disagreement support and simultaneous gates across all screened systems.
Audit incident learning evidence integrity
Audit the complete point-in-time incident-to-postmortem-to-corrective-action lineage, separating missing or contradictory evidence from genuine overdue learning debt without attributing individual fault.
Audit informative metric missingness
Audit whether aggregate metric availability is associated with a governed outcome using permutation inference, bootstrap intervals, practical effect gates, and false-discovery control.
Audit joint metric dependency drift
Detect changes in cross-metric dependence with empirical-copula ranks, random-feature permutation inference, sliced Wasserstein magnitude, and FDR-controlled pair diagnostics.
Audit joint outcome network integrity
Audit whether a company-specific Bayesian joint-outcome network is fit for reliance by validating point-in-time lineage, DAG and CPT completeness, effective support, protected-attribute exclusions, and strictly out-of-time outcome calibration against a baseline.
Audit KPI threshold bunching
Detect a post-target excess concentration immediately above a governed KPI threshold: compare within-unit pre/post local mass and above-versus-below mirror asymmetry, bootstrap whole units, report density bins and a smoothed log-density jump, and explicitly refuse to equate bunching with individual gaming or intent.
Audit metric regime stability
Detect practical structural breaks across aggregate metric histories with recursive max-CUSUM search, moving-block null resampling, and familywise false-alarm control, then identify the defensible baseline regime.
Audit multigroup metric measurement invariance
Audit whether a multi-indicator aggregate management metric measures a comparable one-factor construct across teams, products, repositories, periods, or companies: fit training-only pooled and group PCA loadings, test configural dominance, metric loading cosine, scalar intercept range, residual variance, and untouched-test reconstruction invariance before any group ranking is trusted.
Audit multivariate metric drift
Detect material distribution shifts with reference-fixed quantile bins, PSI, Jensen-Shannon divergence, standardized Wasserstein distance, permutation tests, and FDR control.
Audit onboarding mentorship evidence integrity
Audit point-in-time onboarding cohorts, ordered autonomy milestones, source completeness and corroborated mentorship windows before publishing privacy-safe ramp evidence.
Audit operational alert decision integrity
Audit every point-in-time operational alert evaluation by recomputing fire/suppress decisions and verifying effective policy, cooldown, evidence freshness, context, controls, severity routing, acknowledgement, action and mature outcome lineage.
Audit org health score integrity
Reconstruct the organization-health composite from frozen component evidence and block publication when source completeness, consent, versioning, construct balance, cross-group measurement invariance, redundancy, privacy or leave-one-component stability fails.
Audit organizational change simulation integrity
Audit whether an organizational or technology what-if simulation is fit for reliance by checking point-in-time model lineage, local history, factor support, second-order dependency structure, calibration, scenario reconciliation and individual-level safeguards.
Audit point in time model integrity
Gate an analytical or AI model on point-in-time correctness by auditing actual feature availability, snapshot creation, target-window ordering, outcome resolution, source-record reuse, and embargoed train/calibration/test boundaries, with row and feature diagnostics rather than a generic leakage warning.
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 portfolio dependency value double counting
Reconcile project and dependency business-case claims to governed unique benefit sources under coherent scenarios, quantifying naive, unique, duplicated, and unassigned value before portfolio prioritization.
Audit probabilistic forecasts
Audit whether resolved probability forecasts are accurate, calibrated, discriminating, and better than a base-rate prediction.
Audit probability policy subgroup equity
Audit an aggregate probability-driven policy across governed groups using weighted selection, error-rate, predictive-value, Brier, and calibration disparities; within-group bootstrap uncertainty; practical tolerances; privacy/support abstention; and Benjamini-Hochberg false-discovery control.
Audit proxy metric integrity
Audit whether an incentivized proxy structurally decoupled from outcomes or harmed guardrails using counterfactual residuals, bootstrap break tests, placebos, and multiplicity correction.
Audit recommendation coherence
Audit whether analytical recommendations for the same decision remain comparable, current, supported and coherent after every unique evidence lineage receives one vote split across its claimants, preventing duplicated source data from manufacturing consensus.
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.
Audit ROI denominator integrity
Reconcile each claimed ROI investment denominator with evidenced cost entries, required categories, inclusion fractions, and shared evidence identity, then recompute ROI before a business case reaches prioritization.
Audit root cause traceback evidence integrity
Audit whether an anomaly traceback is complete, point-in-time, multiplicity-controlled and honestly labeled as temporal or causal, including every upstream candidate, path lag, edge identification basis and later root-recovery validation.
Audit scenario tree decision integrity
Audit whether an adaptive management or capital policy is executable rather than clairvoyant: reconcile terminal probability mass, tree depth and unique node ancestry; require identical actions and information releases for indistinguishable histories; reject actions whose declared evidence is revealed only later; and retain unverified scenario exposure.
Audit selective label partial identification
Partially identify event risk, calibration gap, and Brier score when a policy selectively reveals outcomes: retain missing labels, model observed-versus-missing event odds within each decision stratum under a governed sensitivity ratio, propagate Beta posterior uncertainty, expose unsupported strata and label coverage, and fail closed on wide bounds or undocumented decision rules.
Audit service continuity recovery evidence integrity
Audit whether each critical service has a current, independently reviewed recovery plan whose complete capability/dependency path, backup, restore, failover, communications, RTO and RPO were proven in a recent production-representative exercise.
Audit shadow AI inventory integrity
Reconcile the approved AI-service registry against gateway, DNS/CASB, SSO, expense and provider evidence by deduplicating canonical aggregate usage events, then audit registration, status, domain/data-class policy, broker routing, contracts, security/privacy review, telemetry completeness and reported usage/spend.
Audit software supply chain integrity
Audit the deployed runtime software supply chain from application roots through resolved dependency edges: reconcile SBOM freshness, version resolution, source pinning, artifact attestation, support horizon, license policy, vulnerability disposition, evidence coverage and unique application value without treating repository text as provenance or exploitability evidence.
Audit staggered rollout identification
Audit staggered team-by-team adoption with not-yet-treated controls and require every simultaneous pre-period interval to fit inside a governed equivalence margin.
Audit strategic assumption lineage
Audit every aggregate initiative-value claim against a versioned canonical premise, unit, validation period and independent evidence lineage; retain unknown references, detect stale, unverified, conflicting and source-reused exposure, and gate hidden portfolio concentration in assumptions shared across initiatives.
Audit sunk cost escalation
Audit whether cumulative sunk cost predicts aggregate project continuation after project fixed effects, checkpoint time, forward value, success probability, remaining cost, and future irreversibility, with project-cluster bootstrap uncertainty.
Audit technical asset lifecycle integrity
Audit technical-asset lifecycle and value lineage across placed-in-service, assessment and retirement events; detect orphan active assets, stale recoverability evidence, active value links after retirement, remaining book value on retired assets and duplicated value-source attribution without treating engineering activity as accounting evidence.
Audit technology diligence evidence integrity
Audit a frozen technology diligence case against buyer-declared system/domain/claim scope, management assertions and fresh, rights-cleared, independently reviewed point-in-time evidence.
Audit technology financing plan integrity
Audit multi-period technology financing plans as continuous sources-and-uses ledgers: reconcile cash and debt identities, opening-to-closing continuity, period completeness, verified evidence, liquidity headroom and reported versus computed net-leverage covenants without hiding undefined leverage behind a favorable ratio.
Audit technology loss scenario integrity
Audit a technology loss-scenario ledger as a complete, zero-inclusive, point-in-time financial perimeter: reconcile every expected aggregate exposure and source, freeze scenario/currency/price basis, enforce evidence and privacy, and detect economic-loss lineage reused outside an explicit shared-loss group.
Audit technology risk appetite integrity
Audit whether board technology-risk appetite is executable rather than rhetorical: verify approval and point-in-time lineage, reconcile the root to finance limits, cover every aggregate risk unit exactly once, validate an acyclic owner/action limit tree, cap unsupported diversification credit and surface every breach with an executable escalation.
Audit value realization chain integrity
Audit one frozen aggregate cohort chain from eligible strategy scope through implementation, adoption, business outcome, monetization and cash collection, using whole-cohort bootstrap and simultaneous conversion, end-to-end, evidence and support gates.
Audit vendor lock in exposure
Audit whether every vendor-dependent business capability has a complete, scenario-executable exit portfolio; solve minimum-loss set cover exactly inside a governed state boundary, disclose heuristic fallback, and report infeasible-exit probability, expected loss, CVaR, lead time, and value concentration without converting vendor exposure into misconduct evidence.
Audit workforce identity access evidence integrity
Audit the point-in-time chain from an opaque workforce subject through authorized accounts, independent identity evidence, approved least-privilege grants and MFA/device-backed access events.
Build reverse stress scenarios
Solve the minimum standardized bounded combination of adverse driver shocks needed to breach a governed operating or financial threshold, with driver contributions, binding bounds, and single-driver break points.
Calculate analytics calibration liability
Price the hidden financial liability of stale analytical functions from coherent joint calibration-failure scenarios, decision value at risk, loss fractions and remediation costs; calculate expected loss, VaR, CVaR, reserve breach probability, required reserve and exactly reconciled tail contributions.
Calculate analytics portfolio realized ROI
Reconcile the analytics portfolio's realized ROI from unique finance-owned incremental benefit sources, causal-evidence weights, non-overlapping function allocations, implementation/recurring/shared costs and coherent joint scenarios, with positive-value probability and CVaR loss gates.
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 build buy partner npv
Compare build, buy, and partner lifecycle NPV under coherent joint scenarios, explicit strategic option and switching value, downside CVaR, and governed value gates.
Calculate capital efficiency frontier
Construct a monotone concave capital-to-realized-value envelope, estimate each initiative or portfolio company's relative capital efficiency and value gap, and expose diminishing frontier returns without arbitrary weights.
Calculate churn prevention break even
Calculate the absolute churn reduction an intervention must cause to break even, then test aligned baseline/treated scenarios against probability-of-positive-value and portfolio CVaR gates.
Calculate customer concentration technology risk
Quantify the joint tail risk created when customer contribution is concentrated on shared technology, using coherent failure scenarios, non-additive dependency losses, CVaR, and overlapping component sensitivities.
Calculate decision debt liability
Price unresolved management decision debt from coherent joint scenarios for accumulated delay, value at risk, rework, staleness and resolution cost; calculate expected liability, reserve breach, confidence reserve, CVaR and exactly reconciled decision tail contributions.
Calculate earned value forecast
Turn period-level planned value, accepted earned value, and actual cost into a correlated Bayesian CPI/SPI distribution for final cost, completion period, budget overrun, and deadline miss, with classical EAC cross-checks and an early-progress abstention gate.
Calculate engineering unit economics
Calculate uncertainty-aware engineering cost and net incremental contribution per adopted business outcome across aligned scenarios, including quality/run cost and downside-margin probability.
Calculate execution value leakage
Translate incomplete scope, delay-driven value decay, rework and approved-exception costs into coherent expected, reserve-quantile and tail-CVaR execution leakage, with exact decision-level reconciliation to net realized value.
Calculate feature cost to serve
Calculate fully loaded feature cost and CVaR cost per verified adopted account across aligned build-amortization, run, support, usage, and adoption scenarios.
Calculate financial value of modularity
Value modular architecture as a portfolio of exercisable future-change options, comparing architecture-specific cost, lead time, throughput capacity, discounting, value decay, downside CVaR, and the break-even modular investment.
Calculate financing exit waterfall
Calculate a financing exit waterfall across coherent outcomes with debt and transaction costs, preferred seniority, equal-rank pro-rata shortfall, liquidation preferences, participating residual, iterative participation caps and endogenous class-by-class conversion; require a pure no-profitable-deviation conversion equilibrium, exact payout reconciliation and verified security/scenario evidence before reporting stakeholder payout, MOIC, annualized return and downside.
Calculate human AI decision system value
Calculate the complete economic value of a prospectively validated human-AI decision system from coherent volume and loss scenarios after implementation, AI operation, human review and decision-delay costs, with positive-value probability, return-on-cost and CVaR downside.
Calculate incremental cost effectiveness ratio
Construct a probabilistic incremental cost-effectiveness frontier from jointly aligned cost and outcome scenarios; remove strict and extended dominance before calculating ICERs, and select by expected net benefit plus a cost-effectiveness acceptability curve at organization-owned willingness-to-pay thresholds.
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.
Calculate procurement negotiation range
Calculate an uncertainty-aware procurement bargaining zone from independently governed buyer and supplier BATNA economics; protect both reservation prices at explicit confidence levels, derive a bargaining-weight target, quantify ZOPA probability and tail overpayment, and abstain when evidence cannot support an overlap.
Calculate recommendation conflict exposure
Price the expected, reserve-quantile and tail-CVaR regret of a current action when locally calibrated analytical recommendations conflict, using coherent action-loss scenarios and provenance-adjusted support that cannot be inflated by duplicate source lineage.
Calculate risk adjusted npv
Discount aligned scenario cash-flow paths, expose positive-NPV probability and loss VaR/CVaR, then apply an explicit finance-owned CVaR penalty to test a risk-adjusted investment hurdle.
Calculate shadow price of capacity
Calculate lumpy, discrete capacity shadow prices by re-optimizing a scenario-valued initiative portfolio after a governed increment to each resource, with CVaR penalty and explicit exact or heuristic solver status.
Calculate shared assumption risk exposure
Price coherent portfolio value loss when necessary assumptions interact multiplicatively and recur across initiatives; size reserve, breach probability and CVaR, then use exact continuous-integral Shapley attribution to reconcile nonlinear expected and tail loss to the premises creating hidden concentration.
Calculate strategy to cash conversion
Reconcile approved strategy value sequentially through implementation, adoption, outcome, monetization and collection under coherent scenarios, producing mutually exclusive stage leakage, gross and net cash conversion, reserve need, breach probability, CVaR and exact initiative tail contributions.
Calculate technology economic capital
Calculate expected loss, loss VaR/CVaR, unexpected-loss economic capital, capital charge and technology RAROC under coherent finance-owned scenarios; count shared platform/provider loss once and reconcile it to aggregate units with exact or seeded-permutation Shapley allocation.
Calculate technology plan financeability
Calculate whether a technology plan remains liquid and net-leverage compliant across coherent multi-period cash, debt, investment, financing and EBITDA scenarios; derive the exact minimum period-zero unrestricted capital per path, confidence reserve, breach trajectory and CVaR residual funding shortfall.
Calculate technology risk capacity and headroom
Translate technology loss into board-level risk capacity by jointly stressing liquidity, earnings, covenant and capital absorption; report expected loss, exact probability-mass VaR/CVaR, unexpected-loss capital, appetite headroom, binding constraints and the maximum supported loss multiplier before the approved breach probability fails.
Calculate value of delay to decide
Calculate a period-by-period value-of-delay curve that separates prospectively available information from waiting cost and changing action economics under coherent scenarios.
Calculate value of independent analytics challenge
Calculate the economic value of an independent analytical challenge from coherent incumbent and challenged loss scenarios after complete challenge and decision-delay costs, with probability-of-positive-value and CVaR downside gates.
Calculate value of management flexibility
Price only executable management flexibility on one coherent scenario set: compare a frozen static plan, a nonanticipative adaptive policy and a perfect-information upper bound; separate expected flexibility from remaining information value, tail underperformance and tail regret; quantify liquidity-risk reduction; and refuse value when policy integrity, evidence or dominance fails.
Calculate venture milestone efficiency
Compare evidence-adjusted milestone progress and scenario value uplift per cash consumed, preserving efficiency, value, and downside as a Pareto frontier instead of one opaque portfolio-company score.
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.
Cluster process markov archetypes
Discover privacy-eligible workflow archetypes from aggregate Markov transition counts using empirical-Bayes shrinkage, Jensen-Shannon k-medoids, silhouette quality, and posterior assignment stability.
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.
Construct calibration shared evidence graph
Discover stable candidate relationships for shared calibration design by correlating standardized held-out loss improvement across common environments, resampling whole environments, stability-selecting practical edges, controlling sign discoveries with Benjamini-Hochberg FDR and returning connected components without claiming parameter transfer.
Construct deterministic project pareto frontier
Construct the exact practically nondominated frontier and successive Pareto layers across projects, products, vendors, or investments without hiding tradeoffs behind arbitrary score weights.
Construct quality speed cost pareto surface
Construct a stochastic three-dimensional quality, delivery-time, and cost Pareto surface with practical dominance, membership probability, and a transparent maximin navigator.
Construct stochastic pareto frontier
Construct a stochastic Pareto frontier from aligned joint criterion scenarios using scenario-wise frontier membership and pairwise practical chance dominance rather than dominance of point estimates.
Control online alert false discoveries
Control false discoveries across a prespecified live hypothesis stream with LORD++ and an infinite geometric alpha-spending sequence.
Decompose product margin change
Decompose product operating-profit and margin change across volume, price, variable unit cost, and fixed cost using an order-invariant exact Shapley bridge.
Design balanced stepped wedge rollout
Assign teams or other aggregate clusters to capacity-constrained rollout waves with pair-exchange optimization of cumulative causal balance, represented population, and rollout risk.
Design incentive compatible metric contract
Design metric weights and audit rates as a robust Stackelberg contract, anticipating effort, gaming, detection, guardrail harm, and adversarial equilibrium tie-breaking.
Detect operational critical slowing down
Detect early-warning patterns associated with an aggregate system losing resilience before a possible regime transition: locally detrend rolling windows, track rising lag-one autocorrelation, variance, and spectral reddening, compare endpoint shifts with a frozen reference regime, and control multiplicity under a circular moving-block bootstrap.
Discover environment invariant predictive model
Search every nonempty subset of up to eight candidate features for a sparse predictive relationship whose validation residual bias and error remain within governed limits across represented environments, select without touching the test split, and compare the chosen model once against the full model on future-held-out environment data.
Estimate budget contingency reserve
Size an engineering or investment contingency reserve from one coherent joint cost distribution, stress represented scenario probabilities inside a governed total-variation radius, preserve natural offsets, and exactly reconcile robust tail overrun to aggregate cost items.
Estimate cannibalization adjusted feature value
Estimate feature value after posterior cannibalization of legacy contribution, using aligned adoption scenarios, beta-binomial substitution uncertainty, and value plus substitution-risk gates.
Estimate causal value of execution fidelity
Estimate how much outcome value an additional unit of implementation fidelity causes by using randomized enablement as an encouragement instrument, with whole-cluster arm bootstrap, first-stage, balance, negative-control and ratio-stability gates.
Estimate competing delivery risks
Estimate age-conditional probabilities of delivery, cancellation, escalation, or remaining active with Aalen-Johansen competing risks and bootstrap intervals.
Estimate coordination network percolation
Estimate organizational network tipping points under random versus targeted aggregate-unit loss, with weighted connected-component curves, Monte Carlo intervals, and structural-hub diagnostics.
Estimate cost of delay distribution
Translate probabilistic delivery delay into discounted contribution-value loss, permanent value decay, and governed penalties, including expected cost, tail cost, and the probability of material exposure.
Estimate decision reversal probability
Estimate how often planned evidence would reverse the current decision under a correlated Bayesian preposterior model, while separating fragility, regret, and net information value.
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 dynamic execution factor
Extract a direction-aligned latent execution factor from aggregate metric vectors and forecast its level and velocity with a likelihood-tuned local-linear-trend state-space model.
Estimate engineering extreme value risk
Estimate rare incident, delay, loss, or pipeline-duration return levels with peaks-over-threshold generalized-Pareto fitting, bootstrap uncertainty, and threshold-stability diagnostics.
Estimate engineering learning curve
Estimate a team-fixed-effects power-law learning curve with work-size adjustment, cluster bootstrap uncertainty, and a defect-rate quality guardrail.
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 feature incremental value
Estimate rollout value from segment-level treated/control outcomes with beta-binomial uplift posteriors, finance-owned contribution economics, and a probability-of-positive-value gate.
Estimate financing dilution scenarios
Estimate financing dilution with a scenario cap-table waterfall that solves pre-money option-pool top-ups and capped or discounted convertible claims before allocating post-money ownership.
Estimate FX exposure for engineering
Measure base-currency engineering cash-flow exposure across coherent amount and FX-rate scenarios, preserving natural netting, executable hedge payoffs and premiums, expected loss, CVaR, hedge effectiveness, and exactly reconciled currency tail contributions.
Estimate lee bounds under attrition
Partially identify a randomized treatment effect under differential outcome attrition using direction-aware fractional Lee trimming and bootstrap outer bounds.
Estimate liquidity at risk
Estimate liquidity-at-risk, tail funding need, committed-facility exhaustion probability, and residual unfunded shortfall from aligned operating paths.
Estimate longitudinal policy effect MSM
Estimate repeated-intervention regime effects with stabilized inverse-probability weights, an explicit marginal structural model, cluster bootstrap uncertainty, and positivity gates.
Estimate marginal engineering ROI
Evaluate an ordered engineering investment curve increment by increment, stopping at the first increment that misses its marginal ROI or downside-probability hurdle.
Estimate model risk reserve
Calculate an explicit model-risk reserve from the upper weighted quantile of competing approved models' CVaR loss relative to their weighted CVaR, with disagreement and model-level diagnostics.
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 network direct and spillover effects
Estimate direct, neighbor-spillover, and total effects under Bernoulli-randomized network interference using exact exposure probabilities and randomization inference.
Estimate platform network option value
Value when to activate a shared platform under endogenous network adoption with an exact finite-horizon Markov dynamic program; optimize the invest/wait policy by observed adopter state, compare it with every fixed launch date and never investing, and reconcile option value, investment timing, and adoption quantiles.
Estimate portfolio company execution beta
Estimate company sensitivity to an external portfolio execution factor using company regressions, random-effects heterogeneity, empirical-Bayes shrinkage, uncertainty intervals, and systematic variance shares.
Estimate portfolio diversification benefit
Measure coherent portfolio diversification by comparing joint-scenario CVaR with standalone CVaRs and reconciling Euler tail-risk contributions, stress loss, and concentration gates.
Estimate pricing experiment value
Choose pricing experiment arms by posterior future contribution, conversion-harm probability, and a model-conditional perfect-information value upper bound.
Estimate productivity rebound effect
Estimate how much aggregate capacity released by a productivity intervention is absorbed by induced output or workload using stacked matched-cohort log difference-in-differences; separate fixed-output efficiency, induced output and total resource use, audit pretrends, reconcile the log identity, and cluster-bootstrap rebound uncertainty including backfire above 100 percent.
Estimate randomized causal mediation
Decompose a randomized intervention into natural direct and mediated effects with optional treatment-mediator interaction, bootstrap intervals, and total-effect reconciliation.
Estimate real option abandonment boundary
Learn a continuous-state project abandonment policy with cross-fitted least-squares Monte Carlo, explicit salvage economics, option uplift precision, support warnings, and boundary-shape diagnostics.
Estimate risk contribution shapley
Allocate portfolio CVaR loss across initiatives, companies, services, or risk factors with exact subset Shapley values or disclosed sampled permutations while preserving diversification and hedge contributions.
Estimate role adjusted contribution
Estimate role-relative outcome contributions with empirical-Bayes shrinkage, uncertainty, provenance, and cohort privacy.
Estimate software reliability growth
Estimate long-run software reliability growth with a power-law nonhomogeneous Poisson process, bootstrap trend evidence, and future incident exposure.
Estimate staggered policy rollout effects
Estimate cohort-aware dynamic effects of a team-by-team policy rollout against not-yet-treated controls, with a simultaneous pretrend identification gate.
Estimate switchback policy effect
Estimate randomized operational switchback effects with unit and period fixed effects, declared washout exclusions, distributed carryover lags, overlap enforcement, and whole-unit bootstrap uncertainty.
Estimate synthetic control impact
Estimate intervention effects against a constrained donor-weighted counterfactual with placebo inference and donor sensitivity.
Estimate systemic portfolio contagion
Estimate nonlinear financial distress propagation across a directed portfolio network under coherent joint shocks, separating direct from contagion loss and reporting CVaR, convergence, spectral instability, tail attribution, and finite-round loss influence.
Estimate team stochastic frontier
Estimate a Cobb-Douglas team production frontier with half-normal inefficiency, symmetric noise, conditional efficiency, and bootstrap uncertainty.
Estimate threshold policy effect rdd
Estimate a local sharp or fuzzy regression-discontinuity effect for threshold-assigned policies, with weak-first-stage, density-manipulation, placebo, and bootstrap diagnostics.
Estimate transportable intervention effect
Transport intervention effects to a target environment with similarity-weighted random-effects meta-regression, support diagnostics, and leave-one-environment-out validation.
Estimate transportable root cause probability
Estimate how likely a mechanism actually caused an observed failure using transport-weighted Bayesian random-effects MCMC across remediation studies, posterior probability of necessity, convergence diagnostics and mandatory unmeasured-confounding sensitivity.
Estimate value of flexibility
Value a strategy's pre-action signal-contingent flexibility against its best fixed action, including enablement cost, tail loss, and perfect-information headroom.
Evaluate offline policy doubly robust
Estimate a proposed contextual policy's value from logged decisions using cross-fitted outcome models, doubly robust scores, paired bootstrap safety bounds, and overlap diagnostics.
Explain metric shift shapley
Fit a cross-validated second-order ridge response surface and decompose its reference-to-current aggregate metric shift with an efficiency-preserving Shapley allocation, bootstrap uncertainty, and an explicit unexplained residual.
Fit anchor regression shift robust model
Fit anchor regression across declared operating environments, penalizing residual variation predictable from environment anchors over a governed gamma path; choose robustness strength only on held-out worst-environment RMSE; and expose average fit, environment bias, coefficients, and leave-one-environment stability without claiming generic or causal invariance.
Fit cross fitted isotonic recalibrator
Repair monotone probability calibration with pool-adjacent-violators while using cross-fitting and a paired bootstrap to prove out-of-sample Brier improvement.
Fit honest intervention policy tree
Learn an interpretable heterogeneous intervention rule using separate structure, effect-estimation, and untouched policy-evaluation samples.
Fit team behavior regime HMM
Learn persistent privacy-safe team operating regimes and transitions with a Gaussian hidden Markov model.
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 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 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 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.
Infer competing root cause posterior
Rank competing, compound and unknown root mechanisms from company-local resolved incidents using partially pooled Dirichlet-Beta learning, strict temporal holdout scoring, reliability-tempered signals and posterior uncertainty rather than a single brittle traceback winner.
Infer revealed policy preferences maxent irl
Infer aggregate linear state-feature rewards and their implied stochastic policy from sequential demonstrations using finite-horizon maximum-causal-entropy inverse reinforcement learning.
Infer stability selected temporal metric graph
Infer a compact aggregate temporal dependency graph with a chronologically held-out ridge VAR, moving-block coefficient bootstrap, practical-effect stability selection, and false-discovery control.
MCMC project completion forecast
Forecast live task and project completion with a censored Bayesian lognormal model, MCMC uncertainty, dependencies, and finite parallelism.
Measure decision policy realized value
Measure candidate-versus-baseline realized net value from logged decisions with cross-fitted doubly robust policy scores, full action propensities, cluster bootstrap, importance-weight clipping, positivity mass, effective sample size, logging-policy calibration and cumulative value—so Gitrevio can substantiate decision ROI without relabeling correlation as impact.
Monitor forecast calibration eprocess
Continuously monitor binary forecasts for calibration drift with an anytime-valid mixture e-process that does not incur a repeated-peeking penalty.
Monitor sequential intervention experiment
Monitor cumulative binary intervention outcomes with beta-binomial posteriors, Bayes factors, expected regret, and preregistered success, harm, or futility stopping.
Optimize adaptive analytics plan
Choose an exact adaptive sequence of analyses and an outcome-contingent terminal action by Bayesian belief-state dynamic programming, allowing early stopping while enforcing cost, duration, dependency, exclusion and analysis-step constraints and measuring value over the best fixed analysis sequence.
Optimize agentic autonomy portfolio
Choose one manual, approval-required, bounded-autonomous or autonomous operating mode per action class, maximizing scenario net value under hard authorization/reversibility controls, shared-asset loss, reviewer capacity, cost, availability, dependencies and CVaR.
Optimize AI capability resilience portfolio
Choose unaided drills, work rotations, cross-training, dual running, fallback redesign or monitoring per aggregate capability class using exact binomial shortfall, common-provider unique loss, hard readiness/control/capacity gates and a CVaR Pareto frontier.
Optimize AI code assurance portfolio
Choose standard, expert, pair, property, formal or canary assurance per aggregate AI-code change stratum using Beta-binomial defect simulation, unique shared-component loss, hard controls/resources and a CVaR Pareto frontier.
Optimize AI compliance control portfolio
Select reusable AI compliance controls and one plan per obligation using Beta-binomial residual risk, shared jurisdiction loss, exact shared costs/resources and a CVaR Pareto frontier.
Optimize AI configuration rollout portfolio
Select one current or staged rollout plan per AI configuration release, maximizing expected value minus CVaR regret under hard controls, failure ceilings, application concurrency, dependencies, budget and shared resources while computing overlapping application blast-radius loss once from joint survival.
Optimize AI data rights remediation portfolio
Choose license, replace, delete, disable or retrain actions that maximize preserved risk-adjusted AI value under budget, legal/execution gates, dependencies and scarce resources, while pricing scenario CVaR and counting shared lineage contamination once at its weakest residual member.
Optimize AI evaluation value of information portfolio
Select additional AI evaluation plans by multi-stratum posterior-predictive value of sample information, discounting duplicate content and optimizing budget, reviewer capacity, delay, quality lower bounds and expected/CVaR incremental false-deployment loss with exact or disclosed beam search.
Optimize AI inference efficiency portfolio
Choose one governed AI inference efficiency design per workload across semantic caching, retry prevention, batching and unit reduction, maximizing risk-adjusted economic value under quality, latency, scenario availability, shared capacity, dependency, budget and CVaR-regret constraints.
Optimize AI knowledge refresh portfolio
Select one governed periodic refresh policy per unique knowledge source across every dependent AI application, using renewal-theory freshness, shared-source economics, hard access/control/grounding/loss/resource gates and expected plus CVaR scenario regret.
Optimize AI model routing portfolio
Choose one evidenced AI-model route per workload on a value/CVaR Pareto frontier under hard privacy, residency, retention, quality, latency, endpoint-capacity, route-availability, provider-diversity, concentration, budget and dependency constraints.
Optimize AI output IP risk portfolio
Choose keep, scan, license, redesign, replace, exclude or insure policies per aggregate AI-output class using Beta-binomial claim simulation, collectible indemnity, unique provider loss, hard controls/resources and a CVaR Pareto frontier.
Optimize AI privacy utility portfolio
Select one validated privacy mechanism per AI workload to maximize expected value minus privacy-loss CVaR while enforcing exact shared-account RDP composition, utility, latency, controls, dependencies, exclusions, budget and scarce privacy-engineering capacity, with shared compromise priced once.
Optimize AI workflow design portfolio
Select one governed AI workflow graph per use case, maximizing risk-adjusted business value under hard control, success, latency and scenario-availability gates plus shared model/tool/review capacity, dependencies, implementation budget and economic-regret CVaR.
Optimize alert decision threshold
Choose a cost-sensitive alert action threshold using cross-validated decision curves and bootstrap net-benefit evidence against constant policies.
Optimize analytics challenger portfolio
Optimize a budgeted portfolio of complementary analytical challengers over coherent common-mode failure scenarios, residual losses, stochastic review demand, dependencies, exclusions and tail-risk appetite, with exact subset enumeration or a disclosed dependency-closed greedy fallback.
Optimize attention aware alerting portfolio
Choose one governed alert policy per risk class with Erlang-C response queues and Monte Carlo risk, maximizing protected value net of missed/common loss, false-alert interruption, operating cost and CVaR under hard evidence, quality, response, budget, relation and capacity constraints.
Optimize board technology attention portfolio
Select a board technology-attention portfolio under agenda time, assurance budget, resource, mandatory-review, dependency and residual-risk gates while pricing Beta-binomial failures, shared strategic loss and CVaR.
Optimize budgeted initiative portfolio
Select the highest expected-value initiative portfolio under cash and multi-resource budgets while enforcing dependencies and mutual exclusions across aligned business scenarios.
Optimize calibration experiment portfolio
Choose which analytical functions to calibrate next with exact Beta-binomial posterior-predictive value of sample information, result-contingent activation thresholds, false-activation loss, experiment budget/capacity, dependencies, exclusions and exact-or-disclosed portfolio search.
Optimize capability transition network
Plan training and hiring as integral capacity flows through a time-expanded skill network, maximizing multi-period demand value net of transition cost and lead time.
Optimize carbon cost performance portfolio
Construct a dependency-, exclusion-, budget-, and capacity-feasible portfolio frontier across investment, expected and CVaR total cost including scenario carbon price, residual emissions, and performance capacity, with explicit interactions and exact-or-disclosed heuristic search.
Optimize causal release assurance portfolio
Select one release, assurance or hold option per change from prospectively identified Beta-binomial relative-risk effects while pricing delay, failure and shared common-mode loss under budget, scarce resources, mandatory controls, expected-failure and CVaR constraints with an exact or disclosed beam-search Pareto frontier.
Optimize CI assurance portfolio
Choose one baseline, cache, shard, test-selection, flaky-repair, mutation, integration-suite or runner-scale option per assurance unit using only prospective randomized/known-propensity fault-detection and feedback evidence, common random scenarios, controls, relations, budget, implementation and runner capacity, undetected-fault, latency and CVaR gates.
Optimize cloud reserved capacity
Choose an integer portfolio of dated cloud reservations across coherent demand, realization, spot, and on-demand scenarios; price unused commitment and unserved demand explicitly, enforce coverage and capital gates, optimize expected-plus-CVaR cost, and disclose exact versus deterministic supported-set search.
Optimize commercial commitment portfolio
Select decline or one executable contract-term package per commercial opportunity under common delivery scenarios, period capacity, delivery budget, expected penalty, acceptance-cash, liquidity and CVaR gates; value acceptance and relationship economics and disclose exact or uncertified beam search.
Optimize commercial resilience portfolio
Select a budgeted, capacity-feasible technical resilience portfolio directly on a deduplicated commercial exposure graph, combining simultaneous failures and multiple mitigations multiplicatively, enforcing CVaR and critical-loss gates, returning a cost-loss-tail Pareto frontier, and disclosing exact or deterministic beam search.
Optimize contingent technology financing policy
Optimize initial and observed-signal-contingent financing, restructuring or investment-response actions on a coherent cash/debt/EBITDA scenario tree; enforce nonanticipativity, dependencies, exclusions, node budgets/capacity, liquidity and leverage chance constraints, tail funding need, enterprise value and exact-or-disclosed beam search.
Optimize correlated experiment sequence
Sequence pure-learning experiments over correlated intervention effects using conjugate Gaussian updates, Gauss-Hermite lookahead, early stopping, and terminal deployment value.
Optimize cyber control portfolio
Find a budget-, capacity-, availability- and defense-depth-feasible cyber-control portfolio on a nonlinear attack-path graph, recomputing unique-asset expected loss and CVaR under dependencies, exclusions and multiplicative effects.
Optimize deadline recovery plan
Choose a budget-feasible deadline recovery plan over a dependency DAG using correlated triangular task durations, uncertain acceleration effects, common random numbers, probability-gain-per-cost search, and backward pruning.
Optimize decision authority queue policy
Optimize delegation and escalation by assigning one eligible authority option to each decision class while internalizing nonlinear Erlang-C waiting externalities across shared reviewer pools, wrong-decision and escalation loss, coherent demand scenarios, operating cost, utilization-breach probability and CVaR.
Optimize decision calendar
Schedule dependent strategic decisions as information arrives, balancing contingent action value, delay cost, portfolio tail risk, deadlines, precedence, and scarce decision capacity.
Optimize delivery to cash intervention policy
Choose at most one evidence-backed intervention for each aggregate delivery-ready, accepted or invoiced milestone segment; propagate sequential stage mass under shared scenarios and maximize expected collected-cash net value minus CVaR subject to budget, capacity, liquidity and cash-target gates, with exact or explicitly uncertified beam search.
Optimize discount policy
Optimize one aggregate discount option per commercial segment against scenario purchase, retention, service-cost and contribution economics; enforce expected discount spend, delivery capacity, cross-segment rate-gap and downside gates, compare with an explicit zero-discount baseline, and disclose exact or heuristic search.
Optimize distributionally robust action
Choose the action with the best worst-case expected value when scenario probabilities may vary inside a KL-divergence ambiguity set.
Optimize engineering observability portfolio
Exactly select the budget-feasible metric and integration subset maximizing multivariate Gaussian information, then require held-out information retention with bootstrap uncertainty.
Optimize enterprise technology capital plan
Optimize a two-stage enterprise technology portfolio that commits initial capital now and allocates follow-on capital only after observable signals; enforce non-anticipativity, dependencies, exclusions, signal-specific budget/capacity and eligibility, compare with the best one-shot portfolio, quantify option value and CVaR loss, and disclose solver certainty.
Optimize error budget portfolio
Choose dependency-safe reliability interventions under money and capacity constraints using posterior SLO-breach economics.
Optimize financing terms nash bargaining
Select financing terms through exact risk-adjusted Pareto and weighted Nash bargaining: evaluate full-cost founder and new-investor payoffs on identical exit scenarios, convert lower-tail payout into transparent certainty adjustments, enforce company cash, founder control, investor return, downside, evidence and individual-rationality constraints, remove dominated terms and maximize the weighted log product of surplus above governed disagreement values.
Optimize finops commitment portfolio distributionally robust
Select a complete FinOps commitment portfolio that minimizes worst-case expected cost when scenario probabilities may move within a governed total-variation ambiguity radius.
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 global review assignment
Assign an entire review portfolio globally under expertise, conflict, capacity, urgency, quality, independence, and load-balance constraints.
Optimize identity assurance response portfolio
Choose one preauthorized identity-assurance response per aggregate account-risk case by maximizing simulated net access value minus security, false-positive, operating and CVaR costs under budget, capacity, control, availability and due-process constraints.
Optimize incident learning portfolio
Choose immediate remediation or a predeclared experiment-contingent action for each failure mode using Bayesian value of information, prospective test accuracy and causal remediation effects, common scenarios, shared-loss accounting, Pareto search and hard cost, capacity and tail-risk gates.
Optimize insurance retention
Select an insurance retention and limit by minimizing premium plus expected retained loss and a configurable CVaR tail-risk charge under an optional tail-cost constraint.
Optimize knowledge resilience portfolio
Choose one baseline, cross-training, paired-review, rotation, documentation or backup-owner posture per critical knowledge unit using prospectively identified transport-weighted Beta-binomial relative-failure effects, contributor-availability and common-loss scenarios, exact or disclosed beam search, mentor/learner capacity, budget, expected-failure, CVaR and Pareto constraints.
Optimize learning vs earning allocation
Solve the exact finite-horizon Beta–Bernoulli bandit for allocating scarce units between a known earning baseline and uncertain actions that earn and update their posterior.
Optimize license seat portfolio
Choose integer license packs across aggregate seat pools under coherent demand, on-demand price and capacity scenarios; explicitly price unused and unserved seats, enforce budget, coverage and CVaR gates, and disclose exact versus deterministic supported-set search.
Optimize model averaged joint outcome decision
Choose a governed aggregate engineering action across competing plausible Bayesian-network structures using pseudo-Bayesian out-of-time model weights, coherent joint outcome worlds, causal-identification mass, weighted CVaR, worst-model regret, decision stability and the expected value of resolving model uncertainty.
Optimize multi period calibration maintenance
Optimize a finite-horizon analytics maintenance schedule by propagating each function's healthy/degraded Markov belief under passive operation or recalibration, valuing healthy decisions and uncalibrated loss, enforcing period cash and specialist-capacity constraints, and disclosing exact state enumeration versus deterministic beam search.
Optimize multi period capital allocation
Allocate indivisible project funding schedules across every period budget while respecting dependencies, exclusions, uncertain terminal value, discounting, and a retain-capital baseline.
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 org health intervention portfolio
Select an anti-Goodhart intervention portfolio using conservative causal lower bounds on real operating loss, never score movement, with design/transport/fidelity shrinkage, negative controls, interference, shared-loss, equity, resource and CVaR constraints.
Optimize organizational change mitigation portfolio
Choose a dependency-safe portfolio of documentation, cross-training, review redistribution, onboarding, staffing buffers, staged rollout, rollback or migration-support mitigations that minimizes change loss under nonlinear overlap, common risk, budget, scarce skills, recovery deadlines and CVaR.
Optimize post merger technology integration portfolio
Choose retain, bridge, migrate, integrate or retire for every target capability while pricing delayed synergy, retained standalone value, Beta-binomial failures, unique platform loss, resources, budget and CVaR.
Optimize preventive maintenance policy
Optimize preventive replacement or refactoring intervals with Bayesian-scenario Weibull renewal-reward economics and a worst-case cost penalty.
Optimize probabilistic roadmap commitment
Select the highest-value dependency-safe roadmap that satisfies a joint capacity commitment probability and optional tail-overtime limit.
Optimize product mix
Optimize one discrete quantity option per product across a shared cash budget and multiple capacity pools using aligned contribution scenarios, expected value, CVaR downside, and an explicit exact or heuristic solver boundary.
Optimize queue staffing SLA
Invert an Erlang-C queue across weighted demand scenarios to find the lowest expected-cost staffing level that satisfies a wait-time SLA.
Optimize queueing network capacity
Choose minimum-cost integer capacity additions across a routed open queueing network using traffic equations, M/M/c waits, and exact budget dynamic programming.
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 reserve follow on allocation
Solve a two-stage follow-on capital problem: choose how much reserve to hold now, then choose at most one funding tier per company conditional only on the signal partition genuinely observable later, with coherent scenario value, opportunity cost, CVaR, a reserve Pareto frontier, value of available information, and exact-or-disclosed supported-policy search.
Optimize retention interventions by principal strata
Estimate who an optional retention intervention can actually help—not merely who looks likely to leave—from randomized principal strata, then allocate scarce capacity by conservative net value under harmed-stratum sensitivity, budget and fairness constraints.
Optimize risk adjusted technology portfolio
Choose a dependency- and exclusion-feasible technology investment portfolio on an expected-value, cost, shared-loss CVaR and economic-capital frontier, maximizing net value after a finance-owned capital charge while enforcing budget, capital, tail-loss and RAROC hurdles with exact or disclosed beam search.
Optimize risk mitigation portfolio
Select a dependency- and exclusion-feasible mitigation portfolio that maximizes expected net loss avoided within budget and an optional residual-CVaR ceiling.
Optimize roadmap under resource substitution
Choose a value-maximizing roadmap and one explicitly validated native or substitute resource plan per initiative within all capability capacities.
Optimize robust intervention portfolio
Choose a dependency-safe action portfolio that balances expected and worst-case outcomes.
Optimize safe AI routing exploration portfolio
Allocate bounded production traffic to one safe challenger per AI workload by posterior-predictive knowledge gradient, maximizing net learning value under local quality/harm evidence, privacy, latency, provider diversity, shared endpoint capacity, exploration budget, provider concentration and regret CVaR constraints.
Optimize sample size by decision value
Choose a two-arm experiment sample size by Bayesian expected value of sample information after implementation economics, sampling cost, posterior adoption and harm gates, regret, and Monte Carlo recommendation precision.
Optimize selective human AI review policy
Choose one eligible automation or human-review policy per decision segment using a coherent-scenario multi-choice stochastic program over residual loss, complete cost and review hours; enforce complementarity evidence, scenario capacity-breach probability and residual-loss CVaR with exact enumeration or disclosed beam search.
Optimize sequence dependent roadmap
Optimize a dependency-feasible roadmap sequence under category setup time, execution duration and cost, aligned uncertain value, and exponential value decay, with bounded exact enumeration and visible heuristic fallback.
Optimize service continuity investment portfolio
Choose one production-exercised continuity posture per service-risk unit by maximizing retained business value minus direct/common interruption loss, full cost and CVaR under RTO, RPO, residual-risk, control, dependency, budget and resource constraints.
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.
Optimize shared assumption hedging portfolio
Choose a budgeted, capacity-feasible portfolio of validation, option, diversification or mitigation actions against shared business assumptions, combining repeated actions as diminishing remaining-gap closure while preserving cross-initiative reach, multi-premise complementarity, dependencies, exclusions, common scenario costs, positive-value probability and CVaR.
Optimize shared platform investment
Choose a shared-platform option and adopter coalition under budget, capacity, joint scenarios, pairwise network value, CVaR, and individually rational Shapley-informed cost allocation.
Optimize software supply chain remediation portfolio
Choose exactly one accept, patch, upgrade, replace, isolate or remove option per governed component while unique application disruption paths, direct incident loss, transition/operating/upfront cost, license compliance, scenario availability, cross-option feasibility, budget, capacity and CVaR are optimized together with exact or disclosed beam search.
Optimize sovereign data placement portfolio
Choose one executable regional placement per governed data workload by Monte Carlo posterior risk and exact/beam Pareto search under hard residency, KMS, encryption, diversity, latency, availability, relation, budget and capacity constraints.
Optimize stage gate funding
Value project continuation and abandonment by backward induction at each evidence gate, then select a portfolio within initial and expected follow-on capital limits.
Optimize stochastic flow control MPC
Optimize the next delivery-flow control with stochastic receding-horizon model-predictive control, serial queue dynamics, calibrated arrival and capacity scenarios, expected/CVaR cost, switching limits, exact sequence search, and a disclosed beam-search fallback.
Optimize stratified evidence sampling
Allocate a fixed evidence budget across finite-population strata with exact discrete Neyman allocation and quantify precision gained over proportional sampling.
Optimize tail risk budget allocation
Allocate a finite mitigation budget across mutually exclusive component mitigation levels to minimize portfolio CVaR while preserving aligned scenario dependence.
Optimize team topology
Partition the collaboration graph into bounded teams while balancing preserved working relationships, skill coverage, membership stability, and fixed assignments.
Optimize technical asset lifecycle portfolio
Choose exactly one retain, modernize, migrate or retire option per technical asset under common scenarios, unique value-stream capability coverage, full lifecycle cost and obsolescence loss, cross-option feasibility, budget/capacity and expected-loss/CVaR gates; return a cost-value-tail Pareto frontier with exact or optimistic-bound beam-search disclosure.
Optimize technical debt paydown portfolio
Choose a dependency- and exclusion-safe technical-debt portfolio under capacity and cash budgets by discounting compounding recurring drag, failure exposure, remediation effectiveness, risk reduction, and engineering opportunity cost.
Optimize technology risk limit allocation
Allocate scarce aggregate technology risk limits across discrete locally executable operating envelopes, preserving option relations and common loss once; maximize expected net value after a capital charge subject to nominal, expected-loss, CVaR, economic-capital and RAROC appetite, then reconcile selected unit capital with exact or seeded Shapley allocation.
Optimize time consistent capital policy
Optimize a finite multistage capital policy that can actually be followed: attach action bundles to observable scenario-tree nodes, enforce local budgets/capacity plus pathwise dependencies and exclusions, roll scenario cash and terminal enterprise value, constrain liquidity chance and recursively nested conditional CVaR, and disclose exact global enumeration or deterministic beam fallback.
Optimize value realization recovery portfolio
Choose a budgeted, capacity-feasible portfolio of stage-specific value-recovery interventions under coherent scenarios, combining each action as diminishing closure of its remaining gap while preserving cross-stage strategic complementarity, dependencies, exclusions, positive-value probability, CVaR and exact-or-disclosed beam search.
Optimize vendor contract terms
Optimize vendor contract terms across coherent usage, service-credit, exit, and fallback-price scenarios using exact option evaluation, CVaR, Pareto screening, and total-variation probability robustness.
Rank experiments by expected information gain
Rank prospective experiments by Bayesian mutual information and decision-aware expected value of sample information across explicit hypotheses, result likelihoods and decision payoffs; price usability, monetary cost and decision delay, expose recommendation-change probability, and preserve a value-information-cost-delay Pareto set.
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.
Rank management actions
Rank reversible, evidence-backed management actions and separate blocked work.
Rank portfolio companies by risk adjusted progress
Rank stage-comparable portfolio companies by evidence-shrunk milestone value minus a CVaR downside penalty per cash consumed, with weak evidence explicitly unranked.
Recommend safe contextual bandit action
Recommend contextual aggregate interventions with Bayesian reward learning only inside a posterior logistic harm constraint, explicitly falling back to a governed baseline when no arm is safe enough.
Recommend stop continue scale decisions
Recommend stop, continue learning, or scale for aggregate initiatives using beta-binomial posterior rollout economics, independent harm gates, simulation precision, sampling cost, and opportunity decay.
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.
Reconcile plan actual variance drivers
Reconcile plan-to-actual value variance with an exact, order-independent Shapley decomposition of a declared multilinear operating model.
Score evidence readiness
Gate an analytical claim on coverage, freshness, identity resolution, sample size, and source agreement.
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.
Solve bayesian influence diagram
Solve an exact discrete Bayesian influence diagram over actions, chance-node DAGs, action-dependent conditional probabilities, pre-decision evidence, and additive utility tables, then quantify action regret and the expected value of perfect information for observable exogenous nodes.
Solve belief state management policy
Solve a finite-horizon partially observable management problem over calibrated latent operating states and quantify the value of adaptive observation.
Solve budgeted bayesian experiment portfolio
Choose a budget- and resource-feasible portfolio of Bayesian experiments whose correlated observations can change multiple governed deployment decisions.
Solve distributionally robust markov policy
Solve a discounted Markov policy against simultaneous L1 transition-confidence sets derived from empirical state-action counts.
Solve distributionally robust product portfolio
Solve a budgeted product portfolio under ambiguity in scenario probabilities, acyclic dependencies, mutually exclusive choices, and governed pairwise cannibalization or synergy using a total-variation uncertainty set.
Solve entropic risk sensitive markov policy
Solve a finite-horizon Markov policy under exponential downside utility and compare it with the risk-neutral policy using paired Monte Carlo lower-tail CVaR.
Solve robust multiobjective portfolio
Solve a budgeted dependency-safe portfolio against both scenario-probability ambiguity and every vertex of a bounded stakeholder-preference simplex, using governed utility anchors and returning practically nondominated supported tradeoffs.
Solve robust policy across causal models
Choose an aggregate policy across competing interventional causal models and a bounded posterior credal set: derive model-by-action expected utility from outcome probabilities, compute exact lower/upper utility and adversarial model weights, minimize worst-case regret, expose model disagreement and value of perfect model information, and fail closed when any causal evidence gate fails.
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.
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.
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 AI assistant rollout ROI
Value an aggregate AI-assistant rollout from aligned joint causal-effect draws, preserving delivery/time/defect/incident dependence while enforcing identification, out-of-time, overlap, metric-integrity, effective-sample, quality-harm, NPV, ROI, and payback gates.
Value architecture migration option
Value an irreversible architecture migration as a finite-horizon, signal-contingent optimal-stopping policy that cannot see future information; compare its expected and tail cost with never migrating, every fixed migration date, and a perfect-information ceiling, then expose the option value of waiting for real evidence.
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.
Value next round option
Value raising now versus delaying for a milestone by simulating posterior milestone success, bridge-capacity failure, conditional future dilution, terminal stakeholder value, and lower-tail delay loss.
Value of information
Calculate how much it is worth paying for more information before making an engineering decision.