Trust
Data, models, and deployment
Evaluating Cognizable includes deciding where information is stored and processed, which models are used, and who is responsible for operating the system. Some boundaries are already reflected in the prototype; others need to be agreed for a specific deployment.
Our stance on data sovereignty and AI
The understanding people build through their work should remain under their organization's control. Choosing to use AI should not require handing that control to a particular provider or ecosystem.
We are building Cognizable around the ability to choose where data is processed, which models are used, and how those choices can change. Preserved context should remain useful as models, providers, and infrastructure evolve.
That control also matters to the people contributing. They need to understand what is being preserved, who can access it, and how AI will use it. Technical ownership alone does not create that trust; clear boundaries and organizational practice matter too.
Provider freedom does not mean every model is interchangeable or every migration is effortless. It means making dependencies explicit and preserving practical options to change them.
These principles guide the product's development. The sections below distinguish current capabilities, deployment-specific conditions, and work still ahead.
Review what becomes lasting context.
The current prototype lets people inspect a proposed capture and explicitly commit it as durable project context. An unresolved question can be preserved without turning it into an agreed conclusion.
Relevant context can also inform an answer provisionally. Its use is visible and does not automatically add it to the conversation's ongoing context.
Access rules for organizational use need separate consideration. The demonstrated workflow does not yet establish multi-user permissions.
See the walkthroughChoose where model processing happens.
Cognizable uses configurable model endpoints rather than tying preserved context to one AI provider. Model suitability still depends on the task and the capabilities supported by the integration.
For any proposed deployment, model choices need to account for where requests are processed, what the provider retains, whether data may be used for training, and who can access it. These conditions depend on the selected service and agreement.
Make operating responsibilities explicit.
The intended deployment directions are an environment operated for a customer, or an installation within the customer's own infrastructure. Availability and suitability need to be established for the proposed use.
That discussion should cover system access, backups, updates, monitoring, and incident handling, as well as the model endpoints used. Running the application on your own infrastructure does not by itself keep model requests there.
Cognizable does not currently claim independent security or compliance certification.
Keep context useful beyond one model.
Preserved project context is stored separately from the models and derived search indexes used to work with it. This supports changing execution components without treating a provider's chat history as the only lasting record.
A portable export that preserves meaningful context is a design requirement. The export format is still being developed; export and deletion capabilities should be confirmed before an organizational trial.
Discuss your requirements.
Tell us which data boundaries and operating conditions a possible use would need to meet.