PrivacyCloud is Rindogatan's AI-powered Data Clean Room, running entirely on your infrastructure. No data leaves your environment. Ever.
Every clean room on the market asks both parties to upload their customers to a third place and trust the walls. We move the compute to the data instead.
A brand and a publisher (or two business units that legal will not let share a table) ask a question together and get the answer, without either side seeing the other's records. Matching, overlap, and measurement happen inside a perimeter you own.
Most clean rooms work by both parties uploading to a third place. PrivacyCloud inverts that: the compute goes to the data. Your side runs in your environment, on your hardware or your tenancy, and nothing is exported to be joined somewhere else.
The intelligence runs beside the data rather than behind an API. Audience discovery, propensity, incrementality, and natural-language querying all execute on open-weight models you host, which is the only version of this that survives a transfer assessment.
Aggregation thresholds, k-anonymity, and differential-privacy noise are applied by the room itself, so an analyst cannot accidentally ask a question that reidentifies somebody. The controls are architectural, which means they hold when people are in a hurry.
Measure across channels without handing your customer file to the channel.
Sell the value of your audience without selling the audience.
Run retail media on transaction data that never leaves the business.
The engine sits directly on your infrastructure and processes data where it lives. No uploads, no personal data crossing a border, and no model provider in a position to learn your customers on the way through.
Your own servers, your own cloud tenancy, or a hybrid of the two. If the warehouse is the centre of gravity, the room runs next to it rather than pulling a copy out.
Open-weight models sized to the job and, where it pays, fine-tuned on your own campaign and transaction history. No dependency on a single vendor's roadmap or price list.
Differential privacy and k-anonymity applied at query time, with the budget and the thresholds set by your DPO rather than by a supplier's default.
Rooms connect to other rooms. Partners can answer a shared question without either estate exporting a record, which is what makes the model work beyond a single bilateral deal.
If your data already lives on Databricks or Snowflake, the clean room belongs there and we will implement it there, because the right answer is usually the one that does not start with a migration. The platform vendors have built serious collaboration products and we are happy to run them the sovereign way.
What we add is the layer none of them sell: the intelligence inside the room running on open-weight models you host, tuned on your own data, with the privacy controls set by your DPO instead of by a supplier's defaults. That is the part that decides whether a clean room is a compliance artefact or a competitive one.
So the question we start from is not which product to buy. It is which parts of this you should own outright, and the answer is usually the model layer and the rules.
If you arrived here from privacycloud.com, you are in the right place. PrivacyCloud was our vendor-risk and privacy-engineering company, and the writing that came out of it (on cohort-based targeting, on aggregated reporting, on why the individual customer record was going to give way to modelled approaches) is the argument this practice was built on.
The privacy cloud idea did not go away. It grew into something with more teeth: a clean room with a sovereign AI engine inside it, delivered as part of a marketing-operations programme rather than as a product you are left to operate alone.
A partner you would work with if legal allowed it, a measurement question nobody will answer, a data set two teams cannot share. In ninety minutes we map what a clean room on your own infrastructure would make possible, and what it would cost.