Orivian Analytics for Trade Economics
Which commodity earned its working capital last quarter?
Your trading team can answer that in seconds — margin by commodity, channel, and delivery basis, joined to the capital each trade consumed, with every answer traced to its source. Orivian Analytics is governed AI analytics built for commodity trading economics.
See it answer a real trading question
The same governed Ask experience as the municipal demo, on synthetic trading data — commodities, channels, and working capital generated for demonstration, so you can see exactly how it behaves before your data ever touches it.
Demo recording coming soon
“Which commodity earned its working capital last quarter?”
Until the recording is up, we’ll answer it live — on synthetic trading data, so you can see exactly how the platform behaves.
Book a live walkthrough →Built for how trading economics actually work
Trading economics, not GL gymnastics.
Results live at the grain traders actually think in — commodity × channel × delivery basis — instead of being reverse-engineered from account balances. Related-party and third-party channels, consignment linkage, and both delivery bases are first-class dimensions, not report filters.
Margin and the capital it consumes, together.
Net margin per ton means little without the working capital behind it. Every trading result is joined to the capital it tied up, so delivered-terms and cash-and-carry business each carry their true cost — and return on working capital is a governed measure, not a spreadsheet argument.
Questions in plain English.
“Which commodities ran a negative margin last quarter?” “Which delivery basis is eroding margin this quarter?” “How does third-party volume compare to related-party?” “What's our return on working capital by channel, annualized?” Your team asks; the platform answers from the governed model — no report queue.
Answers you can take to the risk committee
Trading decisions and lender conversations are no place for a confident wrong answer. Orivian Analytics is governed end to end:
Every answer cites its source.
Results trace to a named, version-controlled data model — eight governed measures from volume to return on working capital, defined once, in one place.
It declines rather than guesses.
Per-unit and ratio measures don't sum — and the platform knows it. A question that would naively total margin-per-ton across commodities gets declined, not answered wrongly.
Your data stays in your instance.
Tenant isolation and row-level security are enforced at the database layer, not the application layer. Trading books are not something to share an app server over.
Built-in views for the questions that recur.
Trading results by commodity and channel, working capital by delivery basis, margin after partner share, and period-over-period volume — out of the box.
The same governed platform, in a second domain
Trade economics is the second industry domain on the Orivian Analytics platform, alongside municipal government. One governed semantic layer, one security model, one rule: every answer cited, or declined. If your trading results live in spreadsheets that disagree with each other, we should talk.
All demonstration data is synthetic — commodities, channels, and trading results are generated for product demonstration. Your data never appears in a demo environment.