We read AI and cloud bills for a living and turn them into decisions.
The practice exists for a specific problem: teams are shipping cloud, LLM, SaaS, observability, and automation workflows faster than their economic controls mature. Olive One turns that spend into decisions a CTO and CFO can act on.
How we work
Continuity from scoping to delivery
Every engagement stays with the same reviewer from the scoping call through the decision memo - no rotating consultants, and no starting over with someone new partway through.
Why it exists
AI changed the cost surface
Cloud cost control used to focus on infrastructure waste. Production AI adds model calls, retrieval, retries, observability, agent loops, SaaS automation, APIs, and margin risk that do not fit neatly inside one vendor dashboard.
How Olive One thinks
Cloud and AI spend should be tied to business outcomes, not only services and invoices. A useful AI feature can still damage margin if retries, model routing, context growth, logging, storage, or ownership gaps scale faster than successful outcomes.
The core question is not only "where can we save money?" It is: what should the business keep, optimize, cap, reroute, reprice, govern, or shut down?
Olive One follows FinOps Foundation methodology, applied to a newer problem: AI app cost diagnosis and margin leak reviews, where model calls, retries, and agent loops do not fit a cost playbook built for infrastructure alone.
How the practice is structured
One engagement, one deliverable standard
Every engagement is the same product, scoped before work begins:
- Agent Economics Review — one AI workflow and one engineering decision, reviewed read-only from billing exports and sanitised usage data.
The deliverable is a decision memo: cost per successful outcome, the engineering variables driving it, what each option does to the number, and stated confidence and evidence gaps. Inputs are scoped billing and usage exports; the memo is the output. Full method detail is on the Methodology page.
What Olive One does
Agent Economics Review
Olive One reads the invoice and the architecture together and answers one question: what will this engineering decision cost? The output is a decision memo with cost drivers, option comparison, assumptions, confidence, and next actions.
What Olive One does not do
No inflated promises
Olive One is not a prompt-engineering agency, generic AI strategy deck, managed dashboard build, procurement-only cost review, or savings-guarantee offer. It does not claim client logos, certifications, or outcomes that are not public and verifiable.
Sensitive data handling
Olive One does not request credentials, API keys, service-account files, tokens, or secrets at any stage of an engagement. If there is a fit, the only ask is the minimum billing, usage, launch-date, and workflow context needed to answer the economic question.
Preferred inputs are exports, screenshots, sampled usage data, sanitized workflow notes, or scoped read-only reports where available. The goal is data minimization: enough context to make a decision, not broad access to your systems.
Best fit
- B2B SaaS teams with production or near-production AI workflows.
- CTO/CFO teams trying to explain an AI, cloud, SaaS, observability, or automation spend spike.
- Engineering, finance, and product leaders who need one decision memo before building heavier infrastructure or changing pricing.
- Teams willing to share scoped billing and usage context after the scoping call.