The AI layer that binds models and generative workflows to enterprise data.
A model that cannot reach the institutional record can only generalise. AIS is the layer that binds the two together — grounded in governed data, permissioned to the person asking, and accountable for every answer it produces.
Models arrive knowing everything in general and nothing about your institution in particular, and the gap between the two is where most enterprise AI quietly fails. AIS closes it by binding the model to the governed data layer rather than to a copy of it. Retrieval runs against the canonical record, under the permissions of the person who asked — never the model's own — and a source that is not governed is not reachable at all.
A chat window is not a workflow. It produces something plausible, hands it to a person, and leaves every question of accuracy, authority, and record-keeping unanswered. AIS instead runs generative capability as defined workflows inside institutional process — extraction, classification, summarisation, drafting, triage — with fixed inputs, fixed outputs, and a named point where a human signs before anything consequential happens.
Every claim AIS produces is tied to the source fields that support it, so a user can follow any sentence back to the record it came from without leaving the workflow. What cannot be supported is not smoothed over into confident prose. It is withheld, and marked as withheld, because in the institutions Pellenzo serves a fluent guess is more dangerous than a gap that admits itself.
Choosing a model should not be the same decision as choosing a vendor for the next decade. AIS keeps the two separate: the binding layer, the workflows, and the audit record belong to the institution, and the model behind them is a component that can be replaced. The whole layer runs inside your boundary — cloud, datacentre, classified enclave, or fully air-gapped on local weights. Data does not leave in order to be reasoned about.