Interas Research · Whitepapers
Interas Research publishes method, not marketing. Each paper below states an architectural claim, shows the mechanism that makes it true, and names the number or invariant that backs it. We describe what the system does and why; we hold the code. Where a claim cannot be grounded, we do not make it.
Three papers describe Control Desk from three angles: the architecture that keeps the engine domain-neutral, the clustering that keeps AI spend flat as data grows, and the evaluation design that grades an AI against answers it structurally cannot read. Read in order or as needed; they cross-reference where the arguments connect. Each carries a print stylesheet, so a browser's print-to-PDF is the download.
A three-layer split keeps a single engine ignorant of every client and domain; one unchanged core proves itself across two unrelated domains through nothing but field mappings, with purity enforced by automated build guards.
A ten-step pipeline calls AI exactly once per distinct problem class. Deterministic clustering holds AI calls flat at four while records grow thirty-three-fold — a 2,458× collapse at roughly one million rows.
Recall and precision are measured on separate layers so failure attributes to the right one, the answer key is structurally unreadable to the diagnosis path, and the acceptance floors are held as gates rather than reported as results.