Composing SME credit risk across four sellers.
A walkthrough of one real composition: from a plain-language outcome to 92.4% coverage, 11% overlap removed, and a single automated settlement.
Here is one real composition, start to finish. A buyer wants to model SME credit risk in the DACH region. Traditionally that means finding, vetting and integrating several data sources over months. We watched it happen in an afternoon.
The outcome, in plain language
The buyer did not browse a catalog. They stated a job: “model SME credit risk in DACH.” The composition engine read two-sided profiles across the network and proposed a dataset assembled from the sellers who actually move that metric.
Coverage before commitment
Four sellers surfaced: a telco with the widest reach, a second MNO, a retail-loyalty operator and a POS network. The proposal carried an estimated coverage of 92.4% of the requested segment and a per-seller contribution. Crucially, 11% of the composition was redundant overlap — the same SMEs covered by more than one seller — and it was flagged for removal before a euro was committed.
Run in the cleanroom
The buyer’s risk model shipped as a container into each seller’s environment. It trained where the data lived. No raw records moved; the boundary counter held at zero rows exported. What returned was the fitted model’s outputs — the computed insight — after passing each seller’s disclosure gate.
One settlement
On completion, GateHouse attributed contribution across the four sellers by the same coverage-and-overlap arithmetic the buyer had seen, and settled automatically — against the buyer’s cloud commit. The buyer paid once. Each seller was paid for unique contribution. Overlap cost no one anything.