Adaptive Continual Intelligence
Built todayContinual learning
and adaptation after deployment.
Continual learning after deployment. Post-training adaptation across shared services, local devices, robotics, and edge environments.
Rollback, reset, and bounded adaptation as explicit product operations.
Cloud
Shared-service updates with tenant isolation
Local
On-device memory, reset, and erase
Edge
Controlled adaptation on embedded hardware
What Adaptive Continual Intelligence Is
Continual learning after deployment.
The stable backbone stays in place. Plasticity adds new knowledge efficiently. Stability protects what the system already knows. Editability removes specific learned contributions cleanly.
Update without retraining everything
Keep a strong base model in place and move customer-, user-, or device-specific change into explicit update operations.
Undo specific changes
Roll back or remove a single learned contribution instead of rebuilding the whole system around it.
Keep each user's state separate
Isolate per-tenant, per-device, or per-user state so personalization does not spill across shared services or devices.
Add policy only when needed
Attach deterministic rules and evidence surfaces only when the deployment boundary requires hard enforcement.
How ACI Works
Keep the backbone stable. Change only what needs changing.
The operating model is simple: keep the backbone stable, isolate the changing state, make the operations explicit, and keep rollback visible.
Keep the backbone stable
ACI starts from a deployed model that already handles the base job. Post-launch change is scoped so the existing system stays intact.
Isolate the changing state
Tenant-, device-, or user-specific state remains scoped so personalization, rollback, and removal stay practical across every instance.
Apply named operations
Every change to the system is a named, auditable operation. Updates, rollbacks, and removals happen through declared surfaces with full visibility.
Protect declared behavior
Protected outputs and typed constraints define what must stay bounded while the system changes. Stability is evaluated against measurable, protected behaviors.
Core Capabilities
What teams can do after deployment.
Protect important behavior. Add new behavior quickly. Undo the wrong change when necessary.
Protect important behavior
Keep named outputs inside a non-regression budget while other parts of the system change.
Measured against protected evaluation sets for precise, quantifiable quality assurance.
Add new behavior quickly
Add new tenant facts, habits, or rules as lightweight updates alongside the existing backbone.
Services, devices, and user workflows can evolve continuously at the speed the product requires.
Undo a specific change
Remove one learned contribution and return retained state to the right timeline.
Teams get a clean rollback or deletion path for any individual change, keeping the rest of the system intact.
Example
A support assistant for one enterprise tenant.
A new tenant refund workflow is added without retraining the shared service.
Protected safety, compliance, and contract outputs stay within their configured bounds while the new workflow takes effect.
If the workflow is revoked or must be deleted, ACI removes that contribution and restores the correct state for that tenant.
Comparison
Why choose this over retrieval or periodic retraining.
The practical questions are simple: does the model really change, can one learned contribution be removed, can protected behavior stay stable, and does every update require GPU-heavy optimization.
Proof and Evidence
Evidence behind the product.
These results show what the current product surfaces can already support. Each deployment still needs workload-specific validation and calibration.
Shared-service inference
- ACI Inference matches baseline accuracy on public classification tasks while adding tenant isolation, rollback, and deletion on the live surface.
- Tenant isolation holds at 1.0 prediction match after adding a second tenant to the shared service.
- Rollback restores baseline predictions exactly, with deletion verified on the control plane.
Shared-service updates carry accuracy parity plus explicit isolation, rollback, and delete behavior.
Continual updates
- ACI methods reach nearly double the final accuracy of standard continual-learning baselines on streaming text tasks.
- Forgetting stays below 0.01 where common baselines exceed 0.20.
- Editability is near-exact: removal error is negligible versus replay-based approaches.
Continual updates are real, and exact or near-exact editability is real.
Domain-incremental learning
- ACI reaches higher final accuracy than replay baselines on multi-domain visual tasks.
- Edit cost stays near zero across multiple ACI configurations.
Continual updates with near-zero edit cost are supported across domains.
Edge and robotics
- On continuous-control tasks, ACI shows over 12x lower forgetting than standard baselines.
- ACI provides explicit edit and rollback metrics that standard approaches do not expose.
- Best suited as a refinement, safety, and rollback layer on top of existing control policies.
ACI supports bounded adaptation, safety enforcement, rollback, and editability at the edge.
Products
Four products, each built for where the learning happens.
Start with the product that matches where the AI runs: shared service, local device, or edge. Add Safety & Policy when deployment requires hard enforcement or signed evidence.
ACI Inference
Shared services
One shared AI service with customer-specific updates, rollback, and deletion — all from a single backbone.
- One shared backbone with isolated tenant state
- Accuracy parity with explicit isolation and rollback on the live surface
- Update, inference, rollback, and proof through API, CLI, MCP, and operator tooling
ACI Personal Agents
Local devices
Memory, preferences, snapshot, restore, and erase that stay on the user's own device — private by design.
- Local state stays on the device by default
- Built for assistants, wearables, household systems, and desktop software
- Privacy, reset, and erase as first-class product features
ACI Edge Runtime
Robotics and edge
Bounded local adaptation for robots and embedded systems, designed for strict latency, memory, and power requirements.
- Designed for strict memory, latency, and packaging budgets
- Deterministic rollback for any local adaptation
- Safety enforcement for runtime outputs that affect control or actuation
ACI Safety & Policy
Cross-cutting add-on
Hard denial, route restriction, and signed evidence — available when the host product requires enforcement.
- Attach to inference, personal-agent, or edge deployments where enforcement belongs in the product
- Rule enforcement matched to the deployment surface
- Signed proof for denial, route restriction, and rollback that must be verifiable
Starting Configurations
How to start each product evaluation.
Defaults for a credible first evaluation before deeper workload-specific tuning.
ACI Inference
Start with shared-service tenant updates on a strong frozen backbone. Keep memory off until the workload proves it helps.
ACI Personal Agents
Start with the local controller, local persistence, and memory on. Turn memory down only when device footprint is the main constraint.
ACI Edge Runtime
Start with the standard edge profile for general workloads. Add safety enforcement when runtime outputs can affect control or actuation.
ACI Safety & Policy
Select the enforcement type that matches the deployment surface. Richer policy layers are tied to signed proof.
Delivery
How each product is delivered.
The product boundary is straightforward: Inference is a service, Personal Agents are embedded local software, Edge Runtime ships as native artifacts, and Safety & Policy attaches to one of those three surfaces.
ACI Inference
Obtain
Managed Vareon service engagement or licensed private deployment package.
Deploy
Run as a shared-service API with tenant management, health probes, and metrics on your cloud or a private cluster.
ACI Personal Agents
Obtain
Licensed package embedded into the desktop, laptop, or device application.
Deploy
Run through the local SDK with local persistence on the customer machine so personalization, snapshot, restore, forget, and erase stay on-device by default.
ACI Edge Runtime
Obtain
Licensed package for the build pipeline plus generated native runtime artifacts.
Deploy
Build native artifacts with the CLI, then link the runtime into the robot, industrial system, or embedded product.
ACI Safety & Policy
Obtain
Ships with the same wheel and runtime artifacts as the host surface; it is not a separate peer installer.
Deploy
Attach on top of the inference, personal-agent, or edge deployment already in service.
Deployment Contexts
Where this delivers value.
Built for environments where updates, rollback, deletion, and isolation are everyday product requirements.
Inference clouds and model providers
Efficient tenant-specific updates, fast adaptation, and reliable rollback at scale.
One shared backbone plus isolated tenant updates.
Enterprise AI platforms
Rapid domain refresh, streamlined model management, and strong compliance controls.
Fast updates, exact removal, and explicit rollback.
Device OEMs and robotics builders
Fleet updates within bandwidth, power, and safety certification requirements.
On-device adaptation with bounded RAM and latency.
Desktop agents and personal AI companies
Memory, personalization, and reset that stay on the user's device by design.
Private on-device learning where user data stays local.
Regulated deployers
Precise deletion, consistent policy enforcement, and audit-ready evidence.
Item-level reversibility, signed proof, and exact state restoration.
Continual learning, adaptation, and control built into your product.
Pick the product that matches where the model runs. Add Safety & Policy where hard rules and evidence belong.