About
Interas Research
We are the research wing of Interaslabs. Our work asks a narrow question: how much determinism can you preserve in a data-operations pipeline, and what does that make auditable that was not before?
What we are
Interas Research sits inside Interaslabs with a single mandate: publish the methods that underpin Control Desk before they become product features. The research precedes the product, not the other way around. Where we cannot write a whitepaper that stands on its own — one with a reproducible claim and an honest “what this does not claim” section — we do not ship the feature.
The audience for our research is the same as the audience for the product: engineering and operations teams inside mid-market companies who carry the cost of cross-system data mismatches. We write for practitioners, not for a general machine-learning audience.
The working thesis
Most data-quality tooling either avoids AI entirely or delegates indiscriminately to it. We take a third path: deterministic scaffolding around minimal, auditable AI.
In practice this means: detection is fully deterministic, driven by explicit rules with configured severity and scope. Clustering and triage are deterministic. Grading is deterministic. AI is confined to a single step — diagnosis — where it operates against a fixed taxonomy of causes and a fixed menu of fix actions, one call per problem class. When diagnosis fails, the pipeline does not halt; it degrades safely and reports the fallback loudly.
The consequence is that every failure attributes to the right layer. A flaky detection rule lowers recall. A confused AI response lowers category accuracy. Neither can silently distort the other’s number.
How the research relates to Control Desk
Control Desk is the production expression of this thesis. The research program produces three things the product depends on:
- Architecture. The three-layer split — substrate, patterns, client configuration — is described in full in WP1. The substrate is domain-neutral by construction; domains enter only as plug-in data.
- Scale proofs. AI spend is proportional to distinct problem classes, not data volume. WP2 sets out the benchmark methodology and the results at 10,000 entities and at 333,333 entities across three systems.
- Evaluation integrity. WP3 describes the answer-key separation model that makes it structurally impossible for the diagnosis path to see the planted breaks it is scored against.
The whitepapers describe architecture, invariants, and benchmark results in full. Implementation is held.