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AI optimization with dedicated engineering
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Your dedicated FinOps expert delivers savings in your first week—then keeps building, tuning, and expanding AI agents until your FinOps practice runs itself.
AI does the work. Your Wiv engineer delivers results.
To your whole environment scanned and analyzed
To owners on every case and first savings
To detection-to-remediation, running
The execution gap is organizational, not just technical. Your engineer closes both.
Your engineer has done this dozens of times. First savings on the invoice by week one, not next quarter.
A managed pipeline from detection to verified reduction. Recommendations don't cut bills. Execution does.
Everything is built with your team, documented, and handed over. You keep a working practice, not a vendor lock.
Automations for your top cost drivers, built and shipped.
Slack, Jira, ServiceNow, so approvals reach the right owner.
The AI agent, configured on your tags, budgets, and guardrails.
A prioritized backlog with owners, effort, and quantified impact.
Leadership-ready reports on spend, trends, and realized savings.
Your team trained, so the machine keeps improving after handover.
Dozens of automations scan your environment. Every efficiency gap surfaced in 24 hours.
Every recommendation and alert gets a named owner. First savings on the invoice.
Your AI agent in Slack, answering cost questions like a teammate.
Two workflows detection-to-remediation by week six, four by week eight.
Working with the Wiv team helped us set up automation workflows in days instead of weeks. The impact was significant—and the process was painless.”
No. A consultant hands you recommendations. Your engineer ships working automation inside your environment, built with your team and documented as it goes. It’s included with your Enterprise plan, not a separate statement of work.
Typically a few hours a week from one platform or FinOps engineer, for approvals and context. Our engineer does the heavy lifting.
You start read-only — enough to baseline spend across your cloud and AI accounts. Write permissions come later and only per use case, scoped to the specific workflows you decide to automate. Every remediation still runs through your own approval path in Jira or Slack.
Your engineer stays. The first 60 days are the build; after that they keep expanding the workflow library, tuning Wivy to your policies, and reporting realized savings. Everything is documented as it’s built, so your team can run and extend it either way.
A platform that does the work. An engineer who makes sure it lands.
