NeuraFrame sits around existing models and workflows. It preserves verified work and decides whether to reuse, review, escalate, or invoke the model. The same frame is the reuse engine in Studio, a drop-in Gateway in front of any provider, and curated learning across machines with Fleet.
The expensive model teaches NeuraFrame while you build, then it is removed. At run time the frame runs without a model on the machine: it serves verified work from memory in milliseconds and calls a model, or escalates, only when it meets something genuinely new. It says when it does not know rather than guessing, and it uses no confidence scores, because reuse is proven, not scored. This is what lets the same frame run on a robot with no model or GPU on board.
A cache answers one question: have I seen these exact bytes before? NeuraFrame™ climbs a ladder, and each rung reuses more of what the model already worked out. A request is answered at the highest rung that can prove the answer is still right, and only what no rung can prove goes to the model.
One rule holds on every rung: a material change is never silently reused. Loosen a plain cache enough to save money and it starts serving stale answers over real changes; NeuraFrame™ escalates them instead, which is the difference between a cache and a memory you can trust.
"What is a widget" answers the same next week. "What is the weather in Arizona" does not. A cache keyed only on the input would replay the hour-old weather answer as if it were current. NeuraFrame™ gives every stored answer a freshness window: a stable answer is reused for as long as it stays true, and a time-sensitive one (weather, a price, anything "latest") is re-fetched once its window passes. It even learns how fast each kind of question actually changes and tightens the window to match, and that learning can only ever make answers fresher, never staler. If a re-check ever finds the world moved, the contradiction is surfaced and the stored answer replaced, never quietly kept.
For vision models the unit of reuse is not the picture, it is the things in the picture. NeuraFrame™ keeps a visual memory of the entities it has seen, tells the same thing moved apart from another thing that merely looks like it, keeps identity separate from condition (a known object that is now damaged stays the same object, and only the changed region is escalated), and on video separates camera motion from object motion so a pan does not create a new world. On a panning stream it sent the model about one percent of the pixels a whole-image cache sends, with zero stale answers over a real change. Labels are optional throughout: it recognises a structure whether or not it has a name yet.
NeuraFrame™ does not require you to replace your model. It can work around local language models, vision models, document workflows, and repeated AI processes.
Prompting gets a response. Teaching changes the future response. Corrections become future behavior rather than disappearing into a chat history, and a pin lets you fix an answer directly at the gateway: bounded, listed, and reversible, without touching your model.
Work that has been verified is reused; uncertain or novel cases still go to the model or route for review. There are no confidence scores, because reuse is a verified match, not a guess above a threshold.
The same layer runs on a single Jetson at the edge or a multi-GPU x86_64 server in your data center. The more expensive your model calls, the more it saves, so it scales up with your hardware, not only down.
Every install checks whether it is the current release and can upgrade in place, keeping its learned memory, config, and license. A download never quietly goes stale.
If NeuraFrame™ is not licensed, it can enter pass-through mode. Your model still runs directly, but the reuse and routing layer is disabled until renewal.
A live readout of calls avoided, the reuse rate, an estimate of tokens saved, and where each saving came from, on the CLI or straight from the gateway. The call counts are exact, so the value is measured, not promised.
Every reuse, escalation, and veto records its reason. On a robot, every action logs what it stood on, the confidence, and the safety verdict, so a past decision can be explained after the fact instead of guessed at.
Bring a codebook: your codes, your grading tables, your rules. Every distinct thing in the footage is tracked free and coded once from its best views; the model classifies, the engine enforces legality and looks up every grade deterministically. One considered call per real finding, not a nervous guess per frame. Built for when accuracy outranks cost. Complex identification
Reuse verified work in front of your local model, from exact repeats up to entities followed across a video, and code footage against your own standard with codebooks. Docs
A one-line drop-in in front of any provider, with freshness windows, pinned answers, and a live savings readout. Docs
Curated learning across many devices: devices learn, you approve what should spread, and it is distributed back, signed, with version-aware rollout and a dashboard. Docs