Dome Systems

For finance

Tokens are a cost driver. Outcomes are what you pay for.

AI investment will dominate the next decade of unit economics. The companies that endure will be the ones who could explain Revenue per Token, Cost per Token, and Token Burn Multiple without flinching. Dome gives finance the levers to manage all three.

SpeedPlatform TeamsRiskSecurityCostFinance

Cost, with Control

Tokens consumed is an input. Inputs aren't achievements.

Every technology investment is a balance of speed, cost, and risk. AI is pushing speed up, and inference spend with it. The growth-hacking era ended when the bill came due. The tokenmaxxing era will end the same way. The companies still standing will be the ones who were quietly running their Token Burn Multiple down the whole time.

Dome is the lever. Route model traffic on policy rather than provider lock-in. Pool spend across teams instead of fragmenting it across credit cards. Tie token consumption to the outcome it produced, so finance can manage to Tokens-per-Outcome rather than Tokens-Burned.

Say yes to AI investment without losing the unit economics.

Token economics

The numbers that should anchor the conversation

Unit economics translated to the inference layer. None of this is exotic. It is gross margin applied to tokens. What is striking is how absent the math is from the discourse. Companies that would never publish topline revenue without disclosing COGS will happily publish token consumption without disclosing what that consumption produced.

Revenue per Token (RPT)

How much value each token produced. The bridge from cost to value when each call ties to a measurable outcome: a resolved ticket, a generated draft, a qualified lead.

Cost per Token (CPT)

What each token cost to produce. Provider rates, prompt overhead, context bloat, retries: all of it visible in one place, attributable to one budget owner.

Token Burn Multiple

Token spend divided by net new ARR. Under 2× is elite, 2–4× is healthy, 4–10× is concerning, over 10× means the tokens are not yet a productive asset.

What you actually sell

Tokens are the input. Outcomes are the unit.

No customer pays for tokens. They pay for resolved tickets, generated drafts, shipped code, closed deals, qualified leads. A perfectly efficient token consumption profile still means nothing if the outcomes those tokens produce aren't worth anything to anyone. The truer metrics live one layer up.

Revenue per outcome

Drives pricing, packaging, and the salesforce conversation. The number that explains your AI business to a board without reaching for token counts.

Tokens per outcome

How efficient is your inference at producing what you actually sell? This is the lever finance can pull through model brokering, prompt design, and tool consolidation.

Margin per outcome

The destination metric. Tokens-per-outcome shrinks; margin-per-outcome grows. The curve every durable AI business will be bending in 2027.

Where Dome bends the curve

Four levers finance can operate

These aren't theoretical optimizations. Each one is a configuration surface that finance can operate against, with audit data underneath to verify the savings landed and the outcomes held up.

Model brokering

Route inexpensive calls to inexpensive models. Pin frontier traffic where it pays back. The single largest CPT lever, applied without changing agent code or renegotiating per-team provider contracts.

Token visibility

Token consumption tied to agent, workspace, and the outcome it produced. Tokens-per-Outcome becomes a number you can show a board, not a guess reconstructed from invoice PDFs.

Tool consolidation

One gateway across every tool means one place to manage SaaS contracts and credentials. Stop paying per-team, per-tool seat tax. Concentrate spend where it can be negotiated.

Audit as evidence

Finance and compliance both need the trail. Produced as a natural consequence of operations, rather than reconstructed before each audit at a discovery cost that compounds with time.