Claude spend grew 2.5× — reports meant stitching screenshots for a day. Model view exposed 20% on Opus for routine work; default shifted.
A software provider for health, fitness, and wellness serving 19,000 centers across 55 countries and 25 million members, running an AI-first strategy with over 1,000 active AI licenses embedded across its platform and operating model.
When Claude usage grew more than 2.5× in two months, there was no way to tell whether costs reflected valuable adoption or expensive models handling...
“More token spend isn’t proof that AI is working. Less isn’t proof that it isn’t. What matters is whether we’re buying the right level of intelligence for the work. Ramp lets us make that judgment in the same place we manage every other type of spend.”
Operations and membership management software for health and wellness centers.
Spend management platform with corporate cards and accounting automation.
Daxko's AI spend visibility is part of this use case:
Related implementations across industries and use cases
Manual digging for price spikes slowed 9,000 monthly transactions. Now, an AI agent codes expenses while accountants review edge cases.
Hundreds of keys, one invoice line, no owners. Giving each key a name and purpose surfaced a ghost workload and a caching fix within weeks.
Raw pace and power data overwhelmed users. AI now turns millions of daily uploads into instant, encouraging performance insights.
Underperforming agents meant engineers rewriting prompts by hand and guessing. Now a self-correcting system reads scores and rewrites them.
Finding root cause meant stitching logs, traces, and metrics by hand during incidents. Now AI surfaces the hypothesis; engineers verify.
Scattered data and basic coding tools bottlenecked engineers. A 9-agent AI workflow shifts them from writing code to directing AI teams.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
Hundreds of keys, one invoice line, no owners. Giving each key a name and purpose surfaced a ghost workload and a caching fix within weeks.
Creditworthy but credit-invisible: millions of Colombians locked out. Graph ML mapped their behavior instead, making them legible to lending.
Claude spend grew 2.5× — reports meant stitching screenshots for a day. Model view exposed 20% on Opus for routine work; default shifted.
A software provider for health, fitness, and wellness serving 19,000 centers across 55 countries and 25 million members, running an AI-first strategy with over 1,000 active AI licenses embedded across its platform and operating model.
When Claude usage grew more than 2.5× in two months, there was no way to tell whether costs reflected valuable adoption or expensive models handling...
“More token spend isn’t proof that AI is working. Less isn’t proof that it isn’t. What matters is whether we’re buying the right level of intelligence for the work. Ramp lets us make that judgment in the same place we manage every other type of spend.”
Operations and membership management software for health and wellness centers.
Spend management platform with corporate cards and accounting automation.
Daxko's AI spend visibility is part of this use case:
Related implementations across industries and use cases
Manual digging for price spikes slowed 9,000 monthly transactions. Now, an AI agent codes expenses while accountants review edge cases.
Hundreds of keys, one invoice line, no owners. Giving each key a name and purpose surfaced a ghost workload and a caching fix within weeks.
Raw pace and power data overwhelmed users. AI now turns millions of daily uploads into instant, encouraging performance insights.
Underperforming agents meant engineers rewriting prompts by hand and guessing. Now a self-correcting system reads scores and rewrites them.
Finding root cause meant stitching logs, traces, and metrics by hand during incidents. Now AI surfaces the hypothesis; engineers verify.
Scattered data and basic coding tools bottlenecked engineers. A 9-agent AI workflow shifts them from writing code to directing AI teams.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
Hundreds of keys, one invoice line, no owners. Giving each key a name and purpose surfaced a ghost workload and a caching fix within weeks.
Creditworthy but credit-invisible: millions of Colombians locked out. Graph ML mapped their behavior instead, making them legible to lending.