Value Calculator

Build an executive-ready NuralAI ROI case.

Customize inputs, estimate annual and three-year value, then download a branded executive PPTX that summarizes savings, payback, assumptions, and implementation path.

ROI Calculator Governed AI active
Incident timeline

Signal ingested from observability stack

Graph impact: payments API, 4 services

AI plan requires approval: restart pool

Runbook executed with rollback ready

Agent confidence
96policy aligned
Graph CMDB
Actions
Approve runbook Open audit trail

Customize your results

Model the value of AI-native infrastructure, cloud, and ITSM operations with your operating data.

These outputs are illustrative estimates. Validate final assumptions, scope, pricing, and deployment timing with NuralAI before procurement approval.

Inputs

Your total value $0

Run the calculator to generate a detailed ROI model and downloadable presentation.

0%3-year ROI
0 mopayback
$0annual savings
$0risk reduction

Savings breakdown

Cloud optimization, tool consolidation, ticket deflection, engineering capacity, and incident avoidance will appear here.

Value drivers

Estimate value across operational efficiency, experience, agility, and risk.

Operational efficiency

AI-assisted triage, ticket deflection, runbook execution, service-owner routing, and evidence-backed governed autonomous action.

Employee experience

Faster request fulfillment, better self-service, cleaner knowledge, and reduced queue friction.

Organizational agility

Reusable workflows, graph context, governed agent patterns, and faster rollout of new automation paths.

Cloud and tool savings

Cloud optimization, redundant tool consolidation, FinOps action, and executive accountability.

Risk reduction

Lower major incident impact, policy-gated remediation, rollback-ready action, and evidence capture.

Executive reporting

Board-ready view of savings, automation value, risk posture, service health, and implementation milestones.

Download your report

Turn calculator results into an executive presentation.

The PPTX export includes financial summary, value-driver breakdown, assumptions, methodology, implementation path, and recommended next steps.
ROI report workflow Input to executive case
Baseline

Enter cloud spend, tooling spend, ticket volume, incident exposure, and NuralAI cost assumptions.

Methodology

Numbers your executive team can challenge and defend.

The model uses conservative assumptions: 28% cloud waste removal, 50% legacy tooling consolidation, 35% ticket deflection, 25% handling-time reduction, and 30% major incident impact reduction. Adjust assumptions with a NuralAI implementation team before procurement approval.

Cloud savings

Rightsizing, scheduling, policy remediation, commitment optimization, and idle resource removal.

Tool consolidation

Reduction of overlapping service, operations, CMDB, integration, and reporting spend.

Labor efficiency

Ticket deflection and AI-assisted triage reduce manual handling time and escalation waste.

Incident impact

Faster correlation, graph context, and governed remediation reduce major incident cost exposure.

Validate the model

Turn ROI assumptions into an implementation-ready business case.

NuralAI can help validate current-state baselines, first-wave workflows, governance requirements, deployment scope, and executive reporting milestones.

Executive value AI surface

ROI updates as AI actions move from recommendation to realized value.

The ROI experience connects savings, reliability, automation, risk, and governance to the same evidence-backed workflow executives expect to inspect.

Value proof

Inspect the assumptions behind ROI before public claims.

The ROI page now separates assumptions, forecasts, approvals, executed actions, and realized value, so the site stays credible while still speaking AI.

Signal detectedContext graph assembledAI recommendation generatedPolicy gate evaluatedHuman approval capturedAction executedEvidence recorded
AI value model

AI recommendations roll into executive value.

The ROI path ties automation candidates to service reliability, avoided risk, recovered capacity, human approval, and evidence-backed savings assumptions.

NuralAI AI CommandGoverned execution trace
Signal detectedTelemetry, ticket, cost, policy, or exposure signal enters the operating graph.
Graph context assembledService dependency, owner, business priority, change window, and risk context are joined.
AI recommendation generatedPolicy-aware agents propose the next action with model traceability and confidence evidence.
Gate evaluatedHuman-in-the-loop approval, rollback checks, and security controls decide what can execute.
Evidence recordedExecution results, approvals, policy decisions, and value impact become audit-ready records.