Tools

Scope the work before you commit budget.

Three free, self-serve tools: model what production AI actually costs, get an honest read on whether your stack is ready to ship it, and find which system shape fits your problem.

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Calculator

AI Cost Calculator

What AI actually costs to run in production — not the sticker price.

Model list prices are the tip of the iceberg. Retries and context growth routinely push real production cost to 5–20× the per-token rate on the pricing page. RAGRetrieval-Augmented Generation (RAG) retrieves relevant passages at query time and feeds them into an LLM's context. overhead and eval runs add more on top. Model your own usage and see where the multiplier comes from.

A production cost estimate you can defend to finance, with the assumptions shown.

Model your cost →

Scorecard

AI Readiness Scorecard

A fast, honest read on whether your data, infra, and process are ready to ship AI.

Answer a short set of questions and get a maturity tier, plus your weakest dimension named — the gap most likely to sink a pilot before it reaches production.

Your maturity tier, plus the weakest dimension you should close first.

Take the scorecard →

Picker

Architecture Picker

Which system shape you should actually build, from seven questions about the problem.

Most builds are one of six reference architectures plus a second one bolted on, and most of the cost of getting it wrong shows up after launch, not at the demo. Answer seven questions about the ask and the input it runs on. One of them asks which failure you can't survive. You get a primary architecture and a likely bolt-on. The build brief is pulled from what actually breaks in each one.

A primary architecture, a likely bolt-on, and the parts teams skip and regret.

Pick your architecture →

Next step

All three give you a self-serve read. When you're ready for a written, board-ready answer, book the diagnostic.

Book a Ship Audit →