Compose the dataset, not the integration.
Buyers describe an outcome in plain language. GateHouse composes a fit-for-purpose dataset across many sellers, estimates coverage, and de-duplicates overlap so no one pays twice.
What composition does.
Outcome in, dataset out
State the job to be done — "model SME credit risk in DACH." The composition engine reads two-sided profiles across the network and proposes a dataset assembled from the sellers who actually move the metric, not the ones with the best listing copy.
Coverage before you commit
Every proposal carries an estimated coverage of the requested segment and a per-seller contribution, computed against network profiles — so the value is legible before a single euro or query is committed.
Overlap, de-duplicated
The linking graph identifies records that two sellers both cover and removes the redundancy from the price. You pay for marginal contribution, never for the same identity twice.
A graph that compounds
Schema-to-schema linkability improves with every transaction. The composition intelligence and the linking graph are the moat: the more the network transacts, the better every future composition gets.