A focused audit of how your product or team is spending on AI — API usage, prompt/context efficiency, model selection, and caching — with a concrete report on where you're overpaying and what to change.

Most teams running AI features are paying for more tokens than they need to: bloated context windows, the wrong model tier for the task, no caching on repeated calls, or prompts that could be doing more with less. This audit finds those specifically and prioritizes them by savings vs. effort to fix.

Process: review of your current AI usage (API logs, prompts, model choices, architecture), followed by a written report with prioritized recommendations. Where useful, includes rough projected savings.

I run TiviTi in Tokyo and work with this stack daily — this audit reflects the same thinking I apply to my own production AI systems.