A supplier writes: 'prices up 8%, blame steel and freight.' Procurement teams had no defensible way to answer. Did steel actually move that much, on that lag, for that share of the product's cost? The analysis existed only as one-off spreadsheet models that died with the project that built them.
Worse, nobody could see commodity exposure across a whole spend base (which categories ride on diesel, which on copper), so every market shock became a fire drill instead of a forecast.
A tool that tells whether a supplier's price increase is fair, or whether you're being overcharged. A supplier emails: 'we're raising prices 8%, blame steel and fuel.' Is that real, or are they padding their margin? Most companies had no way to check, so they just paid. This breaks a product down into what it's actually made of (so much steel, so much labor, so much fuel) and checks each piece against real market prices, month by month and supplier by supplier, so you can see exactly how much of a price increase is genuinely justified and how much is the supplier helping themselves. The gap becomes a negotiation pack you can defend line by line, and stops increases that were never justified in the first place. I built and owned how it came together, end to end.
Canonicalize the spend
Raw PO and invoice files normalize into a standard shape, and repeated commercial relationships (same item, same contract, month after month) are grouped so prices can be tracked through time.
Decompose into drivers
Each category splits into cost components (materials, labor, energy, freight, margin), each with a driver, a pass-through share, and a time lag. AI suggests structures from the data; humans approve every one before it counts.
Map drivers to markets
Every driver matches against a large index catalog: exact where possible, proxy where necessary, and explicitly 'held flat' where no honest match exists. Coverage and confidence stay visible instead of buried.
Compare expected to actual
The engine rolls indices through the cost structure to compute what each month should have cost, compares it with what was paid, and attributes every gap to a driver, a supplier, and a month, traceable down to the source PO.
Traceability over cleverness
Every variance drills to its source transaction. In a negotiation, a number you can't trace is a number you concede.
Cost models governed outside the codebase
Client cost structures live in stakeholder-editable templates that regenerate the client model through validation. Domain experts own the model; engineering owns the rails.
AI proposes, never asserts
Every AI-suggested structure or mapping carries provenance and confidence, with deterministic fallbacks when the model is unavailable. The numbers clients see are mechanical rollups, not generations.
Honest gaps beat false precision
Unmapped spend, proxy matches, and held-flat drivers are surfaced as coverage audits. Saying 'we don't know this part' is what makes the rest believable.