Quantify the ROI, the risk, and the true cost of every AI agent in your enterprise — then reduce the cost without cutting capability.
AgentQuant turns AI from an unmeasured cost center into a governed portfolio — one executive view of the value AI creates and the cost it consumes.
Built for the analytics and data office, the CFO, and the CISO who all need the same defensible numbers.
Most enterprises cannot answer three questions about their AI: what is it worth, what could it cost us, and what are we actually spending. AgentQuant answers all three.
Value Quantification
Every agent, copilot, and automation gets a defensible business case: baseline, measured outcome, realized value, and confidence level — reviewed the way finance reviews any other investment.
AI Exposure in Currency
Translate agentic AI exposure into money. Every agent carries a quantified risk score — data sensitivity, blast radius, autonomy level, and regulatory weight — expressed as annualized loss expectancy.
Run-Rate Governance
Inference spend is the fastest-growing uncontrolled line item in the enterprise. AgentQuant meters every agent, model, and workload, enforces budgets before overrun, and actively reduces run-rate through proprietary model and prompt optimization — not generic cost advice, not after the invoice.
Every number is reproducible, evidence-backed, and traceable to the telemetry that produced it
Weighted return across all quantified AI business cases
Recomputed continuously from live telemetry
Measured value delivered against the quantified pipeline
Baseline-anchored, confidence-scored
Monthly inference and tooling spend across every agent
Metered at the gateway, per call
Projected annual savings from routing and optimization
Attributed to specific interventions
True cost of each completed agent task or business event
Normalized across models and vendors
Volume eliminated by proprietary prompt optimization, model routing, and caching
Tracked per agent and per model
Return after subtracting quantified agentic risk exposure
Ties value directly to exposure
Expected annual loss from each agent's risk profile
Blast radius and autonomy weighted
Actual versus approved spend by business unit and case
Alerting before overrun, not after
Statistical strength of each quantified value claim
Full assumption and evidence trail
AI programs fail governance not because they lack ambition, but because nobody can produce a number the board trusts. AgentQuant gives the analytics and data office a single, continuously updated view of portfolio ROI, annual value realized, agentic run-rate, and savings — with every figure drilling down to the individual agent, model, and business case behind it.
Ranked by return on agentic investment, with confidence intervals
Agent workloads with the largest monthly savings opportunity
Every return netted against quantified agentic exposure
From board metric to individual model call in three clicks
Live portfolio view
Metering, quantification, optimization, and reporting for every AI initiative in the enterprise
One board-ready view of the value AI creates and the cost it consumes — portfolio ROI, annual value realized, run-rate, and savings run-rate.
Author, review, approve, and re-measure AI business cases in a governed workflow with owners, assumptions, and evidence attached.
Inline gateways meter every agent call — model, tokens, latency, and cost — without changing a single line of application code.
Deterministic models convert telemetry into value, risk, and cost figures your CFO and auditors can reproduce and defend.
Proprietary technology that rewrites, compresses, and routes prompts and selects the right model for each task — cutting run-rate without degrading agent output quality.
Attribute every dollar of AI value and spend to a team, cost center, or business unit with full RBAC and SSO.
Meter, quantify, optimize, report — the full AI value and cost governance loop
Agent gateways capture every model call, token, tool invocation, and outcome across sanctioned and discovered AI.
The engine converts raw telemetry into value realized, risk exposure, and true cost per agent, workload, and business case.
Proprietary model and prompt optimization, routing, caching, and budget guardrails cut run-rate while preserving output quality — enforced inline, not retroactively.
Executive, finance, and audit reporting with reproducible assumptions, evidence trails, and period-over-period comparison.
Route each task to the cheapest model that still meets the measured quality bar — automatically, per call.
Caching, context trimming, and prompt-efficiency analysis remove tokens nobody was ever paying attention to.
Per-team and per-agent budgets enforced inline, with alerts long before the invoice arrives.
Projected annual savings run-rate: $659K
Built for global enterprises that must defend every AI number to a board, an auditor, or a regulator
Secure isolation between business units with dedicated resources
Enterprise SSO with role-based access to financial and risk data
Deploy in your region with full data sovereignty compliance
Run AgentQuant inside your own infrastructure
Attribute AI value and cost to teams and cost centers
Immutable assumption and calculation trails for every metric
Enterprise-grade uptime with 24/7 monitoring
Financial and telemetry data encrypted in transit and at rest
Deploy AgentQuant and give your board, CFO, and CISO the same defensible view of what AI is worth, what it risks, and what it costs — updated continuously, drillable to the individual agent.