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Fixed project · 1–2 weeks

AI Cost & Readiness

Your AI features run on someone else's meter, and token bills are doing to 2026 budgets what EC2 did to 2016. Same discipline as a cloud cost audit — new line item.

The new bill nobody owns

The pattern is familiar. A team ships an AI feature on OpenAI, Anthropic or Bedrock, usage grows, and six months later there's a five-figure monthly line that nobody can map to customers or features. Every request quietly carries maximum context, nothing is cached, and the most expensive model handles work a cheap one could do. It's the AWS bill story again, faster.

What I do

  • Spend audit across your AI providers, mapped to features and, where possible, per-customer unit economics
  • The cheap wins: caching, prompt-size discipline, batching, and routing simple work to smaller models
  • Platform choices — direct APIs versus Bedrock, and what your data-handling promises actually require
  • Guardrails: budgets, alerts and per-feature limits so growth can't ambush you
  • Governance basics — logging, access and what customer data leaves your boundary

Scope, honestly

I do the platform and the bill: infrastructure, cost and controls. I don't tune your prompts for quality or build your models — you know your product better than I do. Where the fix is "use a smaller model here", I'll show you the numbers and your team makes the call.