What kinds of data and insights can Intuizi provide?
The Large Behavioral Model learns from de-identified, real-world signals across physical locations, mobile apps, web browsing, CTV viewing, and transactions. From there, Intuizi delivers structured data streams built to your specifications, modeled expansion of the coverage you already have, and audience segments where your product calls for them. The fields and permitted uses are scoped with you at the start.
How detailed is the data, and where do you have coverage?
Coverage and granularity vary by signal type, geography, and timeframe, so we show you the numbers for the dataset you are evaluating. Location signal, for example, is organized by category, brand, and place, and we state the available history, refresh cadence, and gaps up front.
Can Intuizi work with our existing data and technical environment?
Yes. Intuizi reads your signals in their current format and delivers normalized output to the endpoints you already run, on a recurring schedule. We agree the identifiers, data flow, and delivery format with you before implementation, and nothing moves until that is settled.
How do you address data provenance and privacy?
Every signal reaches Intuizi under a written agreement that requires it to be de-identified or anonymized before we receive it, and we never receive or process personal data. Intuizi is a processor, not the owner of the data, so provenance travels with each signal: the source, the usage rights, and what may be returned to you are set for each use case and shown to you during evaluation.
How can we evaluate whether Intuizi's data adds value to our existing analytics?
Start with a defined question and a dataset or workflow you already trust. We benchmark against it, comparing coverage, signal definitions, and the usefulness of what comes back with your current approach, and the success criteria are agreed before the test starts.