Illustrative scenario
Example: outcome-priced support
20,000 tickets/month · 2% bad accepted · $5 payment per accepted resolution
$24,000/year of modeled exposureVerification ≈ $250/year · break-even 0.02%
Economics
What does bad accepted work cost you?
Estimate what happens when work is accepted that should not have been, what verification would cost, and whether a gate would have paid for itself.
This is not about generic cost savings. It is about the economic consequence of one system trusting a bad intermediate result before the next system acts.
What does this look like in your workflow?
Start with an example:
Your workflow
Your exact numbers stay on this page. We record which ranges people model so we can improve the instrument.
What does one bad accepted unit trigger? optional, add any
Verification cost
Estimated from today’s intro rates: $0.001 per run + $0.00002 per unit per deterministic check. Same constants as the pricing page.Enter your monthly volume and the share of work that gets through but shouldn’t to model the exposure. At least one consequence amount is needed for a verdict; the page never guesses one for you.
Modeled scenarios
Agent pipeline10,000 tasks / mo · 2% bad accepted · $0.50 downstream per bad unit$1,200 / yr at risk · break-even 0.21%
Partial-delivery batch100,000 units / mo in 100-unit batches · 1% rejected · $0.10 per unit$1,200 / yr avoided · break-even 0.05%
Below break-even2,000 units / mo · 0.1% bad accepted · $0.25 downstream−$19 / yr net · you probably do not need Spoolis here
Evidence
The economics behave the way the model predicts.
TEST WORKFLOWS · INJECTED FAILURES · NOT CUSTOMER SAVINGS- 10% rejected → 8.8% lower cost per accepted result
- 1% rejected → 0.7% lower
- 0% rejected → verification added exactly its own cost.
Test this against a real workflow.
If your numbers look above break-even, run Spoolis beside the workflow without changing production.
Try it in the sandbox