Savant / How it works
UNDERSTAND THE PIECES
A model brings intelligence.
Savant carries the context.
The design separates the parts that can change from the records and authority you should keep. These are the architecture and setup goals for the upcoming tier.
ONE SYSTEM, DISTINCT RESPONSIBILITIES
Know what is doing the work.
The model
The intelligence source that generates an answer. Different models can handle the same task differently; a new model needs its own qualification.
The runtime
The software that loads or connects the selected model. It has its own version, hardware needs and failure modes. Savant’s planned adapters work around those differences.
Supporting memory
Approved preferences, records, corrections and context stored outside the model’s weights. The goal is to bring relevant context to the next task without pretending every remembered answer is correct.
Permissions and tools
Permission determines which action is allowed. A tool performs a supported action. A model suggesting an action should not grant itself access to your account or device.
Preferences, records & permissions
The context you choose and the boundaries you set.
Memory, qualification & routing
Prepare relevant context and connect supported intelligence.
A model and its runtime
Qualified against the real hardware, context and workload.
Tools, accounts & devices
Separate approval and outcome records for actions.
THE PLANNED EXPERIENCE
Five steps.
Each one earns the next.
Fitting a model into memory is only one question. A useful setup must also behave well on the tasks, context and ordinary workload you bring to it.
Explore the hardware questions ↗Understand your computer.
Assess available resources and the other work already using them.
Qualify a candidate.
Check context requirements, response time, task quality and recovery—not just whether it loads.
Connect the runtime.
Use a supported model/runtime combination, with its limits made clear.
Prepare supporting memory.
Choose useful records and preferences, then set the relevant permissions.
Try real tasks.
Check outcomes and report limits. If a candidate is unsuitable, choose another or leave the task unqualified.
LEARNING, EXPLAINED
A correction should help next time.
The goal is to preserve an approved correction or a verified outcome, retrieve it when relevant, and check whether it improves the next attempt. That can make supporting memory more useful without changing the model’s weights.
Retrieval still needs judgment. A stale preference, a mistaken record or irrelevant context can make an answer worse. Inspection, correction and removal are part of the planned ownership model.
The website does not retrain a model, store your personal context or connect to an AI provider. The interactive walkthrough uses fictional records to explain the continuity goal.