Token economics and margin discipline as AI scales.
Capability is no longer the constraint — cost is. We make the unit economics of AI legible: what each outcome costs to produce, where the spend goes, and how unit cost falls as adoption rises. The discipline that keeps margin intact while usage compounds.
Most AI programs die on cost, not capability.
The model works. The pilot impresses. Then usage spreads, token consumption compounds, and the bill arrives without a line of sight to the value it bought. Programs stall not because the technology failed, but because no one could defend the economics in front of a finance committee.
We treat the bill as a first-class deliverable. Every outcome gets a cost-to-serve; every dollar of spend is traced to the outcome it produces; and the trajectory of unit cost is modeled before scale, not discovered after it. The result is an AI program a CFO can underwrite — one where adoption rising is a margin story, not a runway risk.
No house product behind the numbers. We model the economics across whatever models, providers, and infrastructure you run, so the optimization serves your margin rather than any platform's consumption targets.
Four disciplines that keep the bill legible.
Cost & token modeling
A defensible model of cost-to-serve per outcome — token consumption, model mix, retrieval, and infrastructure — so price-to-value is known before scale, not after.
Usage instrumentation
Spend traced to the workflow that drives it — by team and process. The bill stops being a single opaque number and becomes a ledger you can act on.
Margin & ROI tracking
Unit economics tied to the outcomes they fund — gross margin, contribution, and return per process — reported in terms the board already uses.
Continuous optimization
A standing practice of right-sizing models, caching, routing, and prompts — pushing unit cost down as adoption rises, with every change measured against margin.
Instrument, model, optimize, govern.
A repeatable operating loop that turns AI spend from a surprise into a managed line item.
Instrument
Meter consumption at the point of work — every call, model, and workflow tagged — so the spend is observable before it is judged.
Model
Translate raw usage into cost-to-serve and unit economics, projected across adoption curves the business actually expects.
Optimize
Drive unit cost down — model routing, caching, right-sizing, prompt efficiency — with each lever weighed against the margin it protects.
Govern
Set budgets, thresholds, and controls so cost discipline holds as the program scales and ownership changes hands.
Unit cost should fall as adoption rises.
Left alone, AI spend climbs in lockstep with usage. Under discipline, the cost of each outcome bends downward — instrumentation, modeling, and optimization compounding into widening margin as the program scales. The curve, not the pilot, is the deliverable.
When the economics have to hold up.
Different owners, one demand: AI that scales without quietly eroding the number they are measured on.
Diligence and value creation that treat AI cost-to-serve as a margin lever — not a soft synergy — and hold up under an investment committee.
Predictable, governed AI spend with budgets, thresholds, and a ledger finance can reconcile — so scale never outruns the plan.
Unit economics that protect runway — knowing the cost of each outcome before usage compounds, so growth funds itself rather than burning it.
Answers, up front.
What is AI unit economics, and how is it different from general FinOps?
AI unit economics is the discipline of tying every dollar of AI spend to a defined business outcome and expressing it as a cost per unit of value delivered — a resolved ticket, a closed claim, a generated proposal. FinOps for AI extends classic cloud cost management to the specific drivers AI introduces: token consumption, model selection, retrieval and context volume, and inference patterns that scale non-linearly with usage. The shift is from tracking what infrastructure costs to governing what each outcome costs & whether that outcome still earns its price as you grow.
Why do AI programs more often fail on cost than on capability?
Most modern models are capable enough to deliver the pilot; the failure surfaces later, when usage scales and the cost curve outpaces the value curve. Spend is rarely instrumented at the outcome level, so leaders see a rising aggregate bill without knowing which workflows are profitable and which quietly destroy margin. The result is a program that works technically but cannot be defended financially & gets paused at the exact moment it should expand.
How do you keep cost from outrunning value as usage scales?
We start by instrumenting spend against outcomes, then build a cost-per-outcome model that exposes the true unit economics of each workflow rather than a blended average. From there we apply optimization where it is safe to do so — right-sizing models, tightening context, caching, and routing — and put governance in place so new usage carries a cost expectation before it ships. The aim is a unit cost that holds or declines as volume rises, with margin protected by design rather than discovered after the fact.
What does this engagement actually deliver?
You receive a cost-per-outcome model for your priority AI workflows, instrumentation that makes spend legible at the outcome level, and a prioritized set of optimization and governance levers ranked by margin impact. The deliverable is decision-grade: a clear read on which workflows earn their cost, where the unit-economics risk sits as you scale, and the controls that keep spend accountable & aligned to value over time.
Begin with a Charter.
A fixed-fee diagnostic that puts a number on the economics — cost-to-serve, the unit-cost trajectory, and the levers that protect margin as AI scales.