CleanDims is the infrastructure for governing the categorical surface of every business system. A canonical reference for every dimension that matters. A request workflow that treats changes as version-controlled infrastructure. Agents that read from synchronised caches and produce confidence-weighted output. The categorical layer, governed in production rather than in retrospect.
Customer segment, vendor name, product category, ticket type, expense code, deal stage. These values are the axes along which the business is analysed, measured, and decided. They are the join keys between systems and the features that models train on. They rarely behave the way reports assume they behave.
CleanDims is the infrastructure that fixes this. It maintains a canonical reference for every dimension you choose to manage, exposes that reference to every system that produces or consumes the values, and gives a named steward the authority and the tooling to keep the reference current. The reference is operationally load-bearing rather than decorative: pipelines validate against it, agents read from it through synchronised caches, downstream consumers see warnings when values fail to resolve.
It is the discipline described in the target state, made real as a piece of running infrastructure.
Accepted values, definitions, ownership, version history, and aliases for every managed dimension. Federated across an organisation so different domains maintain their own dimensions without conflict. Exposed via API to every consumer that needs to read it.
Canonical referenceAgents and pipelines classify against a local cache that knows how stale it is. Every output carries a confidence signal that reflects cache freshness. Consumer-side policies decide what to do with low-confidence output. The layer that makes dimension management operationally load-bearing.
RuntimeRequests, an approval flow, a change log. Routine additions auto-resolve where confidence is high; novel values open requests that surface to the right person; deprecations follow a controlled lifecycle. Five metrics measure whether the system is working.
Workflow & metricsHeads of analytics, data, and AI who have noticed that the categorical surface is the layer their systems depend on most and govern least.
Data engineers and architects building infrastructure that has to remain reliable as agent-produced data displaces human-produced data.
Stewards and governance professionals who need the tooling to make their accountability operational.
For thirty years, dimensional inconsistency was tolerable because the rate at which new variants accumulated was bounded by how fast humans could type. Reactive cleanup worked because the volume fit within what a quarterly project could absorb.
That bound is gone.
Agents produce dimensional values at machine speed and reproduce, faithfully and at scale, whatever convention they were configured with. The approach that has worked, in some sense, for thirty years will not work for the next ten.
The fastest way to understand whether CleanDims is the right infrastructure for your organisation is a conversation. We will walk through your most consequential dimensions, identify where the categorical surface is currently load-bearing without being governed, and discuss what a runtime reference would look like in your context. The demo assumes no prior familiarity with the category.