Explore releases Private intelligence. Built around your world.
Cavumo connects models, knowledge, memory, and agents locally—with a controlled path to improvement.
Cavumo is building the foundation: a proprietary local Engine, connected memory and Org Brain, and specialist models for context, evidence, and improvement. Choose a compatible base model. We bring the system around it.
Choose a catalog model or bring your own. Cavumo connects the rest.
Explore the architectureBehind that journey, one Engine checks permissions and connects the components. Explore how each layer contributes.
Start with a compatible catalog model or bring your own. Its original checkpoint and attribution remain intact.
We’re developing compact proprietary models to help the wider system choose context, assess evidence, and recommend improvements.
Select relevant knowledge and approved memories within a context budget.
Generation 1 · Research preview. Context candidate trained but inactive; Verify and Adapt learned models not trained yet. No specialist is validated for release.
A release review. An engineering decision. A research handoff. Cavumo is designed to give every authorized agent the same relevant, source-linked understanding—so it can produce useful work, not start from zero.
EXAMPLE / PRODUCT RELEASE REVIEWBefore release: evaluation evidence, permission review, and owner approval.
Keep the base checkpoint intact. Publish an adapter only after review.
The candidate assessment is attached; remaining checks need an owner.
A common starting point for the engineering, research, and release agents.
Sources, approved shared decisions, and working notes stay distinguishable. Reading permission never implies sharing or training permission.
Synthetic project and illustrative work product. This is not live agent execution, a model output, or a measured research result.
Choose your model, connect knowledge, and manage memory and agents in one considered desktop workspace. Designed for local operation.
Explore Cavumo StudioPre-release. Public downloads are not available yet.Specialist research, engineering notes, and scoped studies.
Selecting evidence and memory under a strict budget, without widening permissions.
Assessing citation support, uncertainty, and the moments when abstention is the better answer.
Recommending bounded experiments, configuration changes, or no change at all.
We’ll measure quality, latency, token efficiency, and total cost under explicit conditions. Every result should come with its hardware, dataset, baseline, and limitations.
Read the evaluation protocol Proposed specialists. Research targets, not validated models.Run compatible models in your environment. Decide what agents can access, what becomes durable memory, and whether a proposed improvement becomes active.
Catalog models or compatible models you supply.
Documents, memories, and training data stay in customer-controlled environments by design.
A proprietary, licensed Engine and specialist layer connecting the system.
Base models retain their own licenses. Cavumo’s software is proprietary. Commercial terms and offline entitlement details will be published before release.
A device you knew. A rhythm that felt like yours. We’re bringing that sense of agency to intelligence—with local models, private context, and a Cavumo foundation that puts you in control.
Why we’re building Cavumo An object from another era.Local execution is one part of Cavumo. The planned platform also manages permission-aware context, approved memory, shared organizational knowledge, agents, and evaluated improvement. Our specialist models and compiler are research efforts whose benefits must be demonstrated before release.
A private intelligence platform in development. It brings local model execution, connected documents, approved memory, Org Brain, agents, and evaluated improvement into a desktop workspace and developer interfaces.
No. The Cavumo platform and Engine are proprietary, closed-source software intended to be licensed for local use. A compatible model you choose keeps its own identity and license. Using Cavumo does not transfer ownership of its infrastructure.
Local execution is the core product design. Inference and supported fine-tuning are intended to run on your device or a compatible customer-owned machine, with no automatic cloud fallback. An optional management service is planned separately from customer content.
Yes, compatible user-supplied models and a reviewed catalog are planned. Original checkpoints remain intact. Optional adapters will be versioned separately, with explicit backend compatibility checks.
Agents can propose memories; you approve durable personal memory, and designated owners approve shared knowledge. Training needs an explicit grant. An adapter becomes active only after evaluation and approval. Continuous learning means a controlled improvement loop.
Apple Silicon is the first target, including an 8 GB inference profile. Model size, context, and training requirements differ. Training will be capability-gated; an inference-compatible device is not automatically training-compatible.
Public downloads are coming soon. The local development MVP includes inference, knowledge, memory, Org Brain, developer clients and controlled fitting experiments. Research and Benchmarks contain the scoped compiler study and current research status. Public installers and validated specialists still require release gates.
