01
Continuity
Carry useful understanding across interactions so people do not have to rebuild the same context from scratch every time.
Private development
AI should not have to lose the thread.
ContextTrace is building a provider-neutral continuity layer that helps AI carry useful context forward — while keeping provenance, correction, and user authority in view.
The idea
Connect what matters — and carry it forward.
AI systems can be powerful in the moment and still lose continuity across time, sources, corrections, and changing circumstances. ContextTrace is being built to help preserve the useful thread without treating every remembered detail as equally certain or equally authorized.
It is designed as a layer across AI providers rather than a dependency on any single model. The goal is not simply to store more information. It is to make retained context more useful, more inspectable, and easier to correct.
Core principles
Continuity without surrendering clarity or control.
02
Provenance
Keep the relationship between information and its source visible, including the distinction between evidence, inference, and later correction.
03
Control
Preserve explicit user authority over what the system may remember, recommend, draft, or eventually act on.
Provider-neutral by design
The continuity layer should outlast the model session.
ContextTrace is being developed to work across AI providers instead of becoming another wrapper tied to one model. That keeps continuity separate from whichever model is doing the reasoning at a given moment.
Now building
ContextTrace is currently in private development.
For partnership, research, startup-program, or early-stage conversations, contact Neal A. Johnson.