One product,three kinds of team.

Your team runs on AI now. Claude, ChatGPT, Codex, Cursor.

The bills keep growing, but the hard questions don't get easier. What is it costing? What is it producing? And is any of it good?

Sabia answers all three. It's AI work intelligence: one place that connects your AI spend to the work it actually produced.

Let's walk through it with Northwind Labs, an eight-person product team, and their month of September.

This is Spend Overview. In September, Northwind spent about fourteen and a half thousand dollars on AI and produced 1,459 outputs. That's ten dollars per output, down seventeen percent from August.

Modelled spend is calculated from usage and published model rates. Switch to Billed to see what providers actually invoiced. The two never mix, so a number never quietly changes meaning.

The trend shows every day, in spend, tokens, or spend per output, over any date range you choose.

Then break it down by project, by person, or by model, and see exactly where the money goes.

AI Work Tools shows how the team uses Claude Code, Codex, Cursor and Cowork, and how much of that usage is linked to real work. Open any session to see its models and token mix.

AI Applications does the same for the AI features you build yourself: spend, requests and failures for every app and feature.

Provider bills brings in the actual invoices from OpenAI, Anthropic and Cursor, and shows where every dollar went, so finance can reconcile against the real bill.

And every modelled figure traces back to a published, versioned rate card.

Spend only matters next to what it bought. Work Overview shows what the team shipped with AI: pull requests and commits from GitHub, docs, sheets and decks from Google Drive, issues from Linear.

Every output rolls up into a project. Filter to one, or open it.

Here's the Checkout redesign: its artifacts, modelled spend, work sessions, the people involved, and the AI-assisted time behind it.

Open any artifact, like this pull request, to see its whole history: every output, the evidence behind it, and the sessions that produced it.

Outputs is the ledger of every AI-assisted change, ready to export.

Follow a session through and you get the full picture: who ran it, which tool and models, how long it took, where the time went, and every output it shipped.

Every session your team runs lands here, and anything is a keystroke away: search projects, artifacts and sessions from anywhere.

Cost and output still leave one question: is the work any good? Quality Overview tracks checks on your own AI applications, evaluations of your team's work, and human reviews, side by side.

Evaluations are rules that Sabia runs on every output of a kind. Tags classify the work. Pass-fail checks score it.

Say a good pull request is one that gets merged with its checks passing. Pick the source, choose the fields, set the rule, and preview it on a real example before you save.

From then on, Sabia checks every pull request, and shows what passed, what didn't, and the evidence for each result.

Sabia. Connect a source. Understand the work. Improve how AI gets used. Book a demo at sabiapartners.ca.