AI cost, routing & return
Inference spend, model routing, workflow economics, and whether AI-assisted work is actually paying.
24 of 388 tools.
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 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 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 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.
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.
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 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 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.
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 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 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 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 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.