ACI InferencePrimary product
Update one tenant inside a shared service
Use ACI Inference when a shared AI service needs customer-specific updates, rollback, and deletion without cloning or retraining the whole model stack.
ACI · Continual Learning After Deployment
Post-launch problems that retraining can't fix.
Every ACI use case starts from a post-launch problem: customer-specific updates in shared services, local memory and erase on personal devices, or bounded adaptation at the edge.
Safety & Policy attaches only where hard enforcement belongs in the product.
Representative Use Cases
Where teams use ACI.
Product and operating problems ACI solves after launch.
ACI InferencePrimary product
Keep enterprise copilots reversible
Enterprise copilots often need frequent domain updates plus a clear record of what changed. ACI keeps those updates explicit and reversible.
ACI Personal AgentsPrimary product
Local memory for desktop and laptop agents
Use ACI Personal Agents when memory, reset, and erase should stay on the user's own machine instead of a central service.
ACI Personal AgentsPrimary product
Private personalization on personal devices
Phones, wearables, assistants, and household devices can personalize locally while keeping snapshot, restore, and erase under explicit user control.
ACI Edge RuntimePrimary product
Controlled adaptation on robots and edge devices
Use ACI Edge Runtime when robots or embedded devices need local adaptation under tight latency, packaging, or certification limits.
ACI Safety & PolicyAdd-on
Hard rules at the model boundary
Add ACI Safety & Policy only when the deployment needs deterministic enforcement, route restriction, or signed evidence inside the product boundary.
How ACI Works
Explicit operations after deployment.
The operating model is simple: keep the deployed model stable, isolate the changing state, make operations explicit, and keep rollback visible.
01
Start from a deployed model
Keep the existing model stable and move frequent change into explicit update operations.
02
Isolate the changing state
Keep tenant-, device-, or user-specific state scoped instead of cloning the whole model for every instance.
03
Apply explicit operations
Every change is a named, auditable operation with full visibility.
04
Protect declared behavior
Declare what must stay bounded while other parts of the system change.
05
Remove scoped contributions
When a workflow, preference, or policy item must be reversed, ACI provides a concrete rollback or removal path.
Core Verbs
Named operations describe the control surfaces. Updates, rollbacks, and removals are auditable and visible.
Product Map
Three products, each defined by where the change lives.
Start with the product that matches where the AI runs. Add ACI Safety & Policy only when the deployment needs hard enforcement or signed evidence.
ACI Inference
Shared services
Update one customer, tenant, or workflow inside a shared AI service without retraining the whole model stack.
- One shared service with isolated tenant state
- Public evidence supports accuracy parity plus explicit isolation and rollback on the live surface
- Keep memory off until the workload proves it helps
- Delivered as a managed service or a private deployment package
ACI Personal Agents
Desktop, laptop, and personal devices
Keep memory, reset, snapshot, restore, and erase local on laptops, desktops, and personal devices.
- Local state stays on the device by default
- Supports desktop agents, assistants, keyboards, wearables, and household systems
- Useful when privacy, reset, and erase are first-class product features
- Delivered as an embedded local software component
ACI Edge Runtime
Bounded edge adaptation
Bring controlled adaptation and rollback to robots and embedded systems that cannot depend on repeated cloud retraining.
- Designed for strict memory, latency, and packaging budgets
- Deterministic rollback for any local adaptation
- Safety enforcement when outputs affect control or actuation
- Delivered as native runtime artifacts
ACI Safety & Policy
Cross-cutting typed enforcement layer
Add hard rules and evidence only when the product boundary requires them.
- Attach to inference, personal-agent, and edge deployments only when enforcement belongs in the product
- Enforcement type matched to the deployment surface
- Keep signed proof explicit where denial, route restriction, or rollback must be provable
- Enabled through the host surface rather than a separate standalone product
Starting Configurations
How to start each evaluation.
Shortest paths to a credible first evaluation before workload-specific tuning.
ACI Inference
Start with shared-service tenant updates. Keep memory off at first, then enable it only if repeated recall measurably improves the workload.
ACI Personal Agents
Start with the local controller, memory on, and local persistence enabled. 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 directly affect control or actuation.
ACI Safety & Policy
Select the enforcement type that matches the deployment surface instead of leaving the method implicit.
Deployment Contexts
Where ACI is strongest.
ACI is strongest where post-deployment change, rollback, deletion, and isolation matter more than another round of full-model retraining.
Inference providers and model platforms
One shared service with isolated tenant state is most useful where tenant count grows faster than teams can manage per-tenant model copies.
Enterprise AI teams
Enterprise deployments benefit when domain refresh, deletion, and rollback have to be explicit rather than hidden inside ad hoc retraining cycles.
Desktop and laptop agents
Local agent memory matters when users expect routines, preferences, and reset/erase controls to stay on the machine they use every day.
Device OEMs and robotics builders
Compiled deployment matters when RAM, latency, power, and certification constraints shape what can change after shipping.
Personal AI products
Local personalization is relevant where privacy, retention, and erase requirements make centralized collection the wrong architectural default.
Regulated or safety-critical systems
Typed constraints, signed evidence, and explicit rollback are relevant when policy enforcement must remain a first-class system surface.
Model Attachment
Which kinds of systems ACI fits best.
ACI operates at the post-deployment change layer across multiple model types. Start where the evaluation target is concrete, then expand only where the workload justifies it.
Language systems
LLMs and text generation
Start with structured tasks — classification, extraction, tool routing, ranking. Keep memory opt-in until recall-heavy evaluation shows a measured lift.
Vision systems
Classification, detection, segmentation
Targets are concrete: labels, bounding boxes, masks. Bind and adapt directly against labeled evaluation sets. Rollback stays straightforward.
Tabular and structured models
Regression, ranking, scoring
Low-cardinality outputs with measurable targets. ACI bind and adapt operate on feature-target pairs with explicit evaluation metrics and deterministic rollback.
Time-series and forecasting
Prediction, anomaly detection, monitoring
Temporal data with measurable prediction error. Adapt to regime changes, roll back when a new regime proves transient, and track drift over time.
Robotics and control
Policy adaptation within safety envelopes
Bounded local adaptation with safety enforcement on control paths and rollback tied to a known-safe policy state.
Audio and speech
Speaker adaptation, recognition, synthesis
Speaker-specific state binds locally. Erase-profile removes speaker data exactly. Evaluation targets are measurable — word error rate, speaker verification, signal fidelity.
Multimodal systems
Cross-modal reasoning and generation
Attach at the task-output level where evaluation is concrete. The same structured-first principle applies: start where targets are measurable, expand with verified objectives.
Recommendation systems
User preferences, content ranking
Per-user preference state binds and unbinds explicitly. Reset and erase are exact. Proof stays available when teams need to inspect what changed for each user.
Start with the use case that matches the operating problem.
Find the product surface that matches how your AI runs through industry pages and documentation.