The review method behind AI app cost emergencies.
Bill Shock Scanner is Olive-One's review methodology for finding cost, ownership, and operational risks before accepting, scaling, repricing, or rebuilding an AI app. It is not a SaaS product, dashboard, or installable tool.
What the scanner does
For founders, the method supports AI App Cost Emergency Reviews before accepting delivery or scaling usage. For SaaS teams, it supports AI Spend-to-Margin Audits for production features. The method connects usage data, cloud spend, workflow behavior, retry paths, vector usage, observability spend, and ownership gaps to produce a root-cause report and prioritized actions.
Inputs
Scoped exports and context
Billing windows, LLM usage exports, cloud service spend, vector or retrieval usage, observability spend, workflow notes, launch dates, owner context, and adoption signals. No credentials or secrets are needed for the first call.
Analysis
Spend-to-workflow mapping
The Bill Shock Scanner maps spend movement to workflows, services, models, retries, agents, storage, logs, owners, and margin-risk patterns.
Output
Decision report
The output is a report with baseline, attribution, owner map, margin-risk findings, and prioritized decisions: keep, cap, reroute, reprice, rebuild, or shut down.
Who this is for
Founders and technical operators
For teams preparing to hand off AI apps, MVPs, automations, and agent workflows where cloud, LLM, vector, observability, ownership, or operating cost is unclear.
Who this is not for
Not for early experiments
Not for early AI experiments with no production usage, generic prompt-engineering work, dashboard implementation, or teams with no bill spike, usage data, or finance question.
How the method is used
- AI App Cost Emergency Review — one AI app, MVP, automation, or feature with a real bill, delivery, or margin question.
- AI Spend-to-Margin Audit — one product area with production cloud and AI usage, and a CFO-visible cost question. Starts at $8,500.
- Ongoing cost visibility — available after an initial audit for teams needing monthly cost review and executive reporting.
Deliverables
What the scanner returns
Cost baseline, LLM/API risk findings, cloud/service observations, owner map, prioritized fix list, and a CFO/CTO-ready executive memo.
Timing
48-72 hours for the entry review
Designed as a fixed-scope review so a founder can make a clear decision about cost, ownership, and operating assumptions.
Fit / no fit
| Fit | No fit |
|---|---|
| Production or near-production AI, cloud, SaaS, observability, or automation workflows. | Early AI experiments with no production usage or finance question. |
| A visible bill movement, margin concern, owner gap, or pricing decision. | Generic prompt engineering, model benchmarking, or dashboard implementation. |
| Ability to share scoped billing, usage, launch-date, and workflow context after the fit call. | Requests that require broad production access, secrets, or unmanaged credentials. |
FAQ
Is this a dashboard?
No. This is a diagnostic method used inside a human-led review. A dashboard may come later, but the first goal is to explain the risk and decide what to do.
FAQ
What decisions does it support?
Keep, optimize, cap, reroute, reprice, govern, or shut down a workflow based on margin impact, implementation effort, and ownership risk.