The questionWhere does our data go, and could it train a competitor's model?
Operator databases are never copied into Digitata Networks' cloud or any third party's. Agents query them in place with read-only credentials. Where a frontier API is used, the payload is prompts and query results only, under terms that contractually exclude training, and where that is not enough, self-hosted models make the containment physical rather than contractual.
What we can evidence
Keep your databases where they are.
Nothing is copied into Digitata Networks' cloud or anyone else's. Agents query in place using read-only credentials, so the data never leaves the system of record to be analysed.
Send query results, never database exports.
When a frontier model API is used, the payload consists only of the prompt and the specific query results needed to answer the question — governed by commercial terms that contractually exclude model training, with enterprise zero-data-retention options available.
Make containment physical where contracts are not enough.
The self-hosted model option means nothing leaves the perimeter under any circumstances. Retrieval, embedding generation and vector search already run locally on the platform host, so a fully sovereign deployment is a configuration, not a bespoke build.
Why architecture beats assurance
Data protection policies, training-exclusion clauses and vendor assurances all have their place, but they are supplementary to architecture, not substitutes for it. A contractual promise not to train on your data is only as durable as the counterparty, the jurisdiction and the next acquisition. A network boundary that the data physically cannot cross is durable regardless. Both are worth having; only one of them survives a change of ownership.
The competitive risk operators actually worry about
The concern raised most often is not general privacy — it is that operational intelligence shared with a hyperscale inference provider could train proprietary network signatures into a model that subsequently benefits a competitor. It is a reasonable concern, and the honest answer is not reassurance: it is an architecture where the question does not arise. Query results under a no-training agreement is the pragmatic middle. A self-hosted model is the answer where the risk is judged unacceptable.
Every answer carries its evidence
Retrieval-grounded generation is not only an accuracy measure — it is a verification mechanism. The agent cites the specific data it used, surfaces any data-quality caveats it hit during retrieval, and presents an explicit evidence trail. Operators are not asked to trust the AI; they are given the means to check it. An answer you cannot audit is not an answer you can act on.