Constrained optimization
Pick the best feasible option under real limits — budget, headcount, dependencies, capacity — rather than ranking a list and hoping it fits.
105 of 388 tools.
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.
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 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 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 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 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 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 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 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 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 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 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 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.
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 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 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 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.
Estimate engineering portfolio VAR
Estimate correlated cost, schedule, success, value-decay and portfolio downside VaR/CVaR with initiative tail attribution.
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 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 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 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.
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.
Fit honest intervention policy tree
Learn an interpretable heterogeneous intervention rule using separate structure, effect-estimation, and untouched policy-evaluation samples.
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.
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 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 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 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 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 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 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 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 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 global review assignment
Assign an entire review portfolio globally under expertise, conflict, capacity, urgency, quality, independence, and load-balance constraints.
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 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 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 multi period growth budget saturation
Allocate aggregate growth capital across channels and periods on coherent common scenarios while preserving channel-specific Hill saturation and carryover state: search discrete spend schedules, propagate contribution and unrestricted cash, and maximize expected net incremental value minus CVaR shortfall subject to total/period budgets, liquidity and contribution-probability gates, with exact certification or disclosed deterministic beam search.
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 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 receivables intervention policy
Choose at most one evidence-backed action for each lawful aggregate receivable segment, propagate open/disputed payment and default mass period by period on coherent market/cash scenarios, and maximize expected collected-cash net value minus CVaR shortfall subject to intervention budget, capacity, relationship loss, liquidity and collection-probability gates, using exact enumeration or disclosed deterministic beam search.
Optimize regime contingent growth capital policy
Choose one action for each observable recurring-revenue regime and reuse it on every matching future, charging unique commitment resources once; evaluate every policy on coherent regime paths with multiplicative ARR, cash-burn and full action cost, then maximize expected terminal ARR value plus cash minus CVaR shortfall subject to liquidity, target-ARR, dependencies, exclusions, budget and capacity.
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 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 real options
Optimize continue, defer, abandon and expand decisions across staged initiatives using Bellman recursion and current capital rationing.
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 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 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 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 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 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.
Optimize workforce policy tree
Optimize staged team/role capacity actions through uncertain demand by Monte Carlo backward induction.
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.
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 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 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.
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.
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.