Most AI security pages stop at vendor-managed cloud controls and warm language. ChtSafe adds ShinrAI before third-party inference, optional client-side encryption, encrypted retention, and deployable boundaries across chat, media, Team Members, Sims, and API workloads.
If an AI tool is still a shared wrapper on top of a public-cloud LLM tenancy such as Azure OpenAI, the trust boundary did not really move. ChtSafe is built to move that boundary earlier with ShinrAI, optional client-side encryption, encrypted retention, and deployment choices that can put the full runtime on dedicated or air-gapped hardware.
Before provider access: ShinrAI can transform, minimize, or shield sensitive context before third-party inference starts.
Beyond hosted SaaS: the product is designed for hosted, own-instance, dedicated-hardware, and full air-gapped delivery instead of a single wrapper model.
Across every surface: the same protection story carries across chat, media, Team Members, Sims, workflows, and the OpenAI-compatible API.
Most privacy tooling stops at transport or storage. ShinrAI works at the semantic layer—on meaning—before third-party inference, so routed requests can stay coherent for the model without exposing recoverable identity or raw context.
On a ShinrAI-enabled path, sensitive structure can be identified and substituted before content leaves your device or secure gateway. The provider receives input it can reason about; decryption material for restoring your original context is not held in recoverable form by Innovius, the model provider, or the gateway.
Identifies entities, patterns, and semantically sensitive structures across languages and regional formats in real time.
Replaces identified content with coherent substitutes that preserve reasoning validity for the AI provider.
The transformed request follows a correlation-resistant path. No single routing component should hold enough context to reconstruct origin and content together.
What leaves the boundary, where it is transformed, and who controls the hardware across apps, automations, and API workloads.
TLS, encrypted storage, access restrictions, and incident handling are expected. On top of that, workloads can use full client-side encryption so that storage and transit carry ciphertext under keys that never leave user or client custody—operators see policy and routing metadata, not a reusable master key for your content.
ChtSafe changes the request path itself. Sensitive context can be transformed or minimized before a third-party provider receives the routed model request—without asking you to trust a shared tenant-wide key hierarchy for that step.
Customer-specific environments can run on physically separate hardware appliances rather than sharing a generic cloud runtime with unrelated tenants.
ChtSafe supports managed private rollout, dedicated hardware, full air-gapped deployment, and a go-ready hardware appliance model for local environments.
Switch between the three delivery models buyers actually choose: ChtSafe.com for smaller teams, Own Instance for isolated or hybrid rollout, and full On-Premise when the whole stack must stay local.
Fastest route for smaller teams that want secure multi-model access without operating their own instance.
Requests hit ChtSafe first. ShinrAI policy and routing act before third-party inference starts.
Users work in ChtSafe.com or the hosted portal.
Identifier shielding, policy, and route decisions happen before provider inference.
Selected external models can be used, but only through protected routing.
History, logs, and hosted retention stay under ChtSafe policy.
Only the routed provider request, not a plain identity-linked request or a standing tenant decryption capability.
Innovius operates the portal, billing layer, ShinrAI routing surface, and hosted retention boundary.
Smaller teams that want immediate rollout with stronger trust boundaries than a plain SaaS wrapper.
Best when a company wants its own isolated instance and can place parts of routing, agents, or inference locally.
The customer gets a separate deployment boundary. Policy, agents, or selected inference can move closer to customer-controlled systems.
Users enter a customer-specific deployment rather than the shared SaaS surface.
ShinrAI, agents, and connectors can run in a separate tenant or partly on customer-controlled systems.
Workloads can mix local inference, remote providers, or both depending on policy.
Retention and operational controls align more closely to the customer environment.
Agent harness components, connectors, policy services, and selected inference paths can run on customer-side infrastructure.
Remote providers remain optional when the workflow chooses them; hybrid design is intentional here.
Organizations that need a separated instance and stronger isolation without going full air-gap.
For sovereignty-critical deployments where runtime, storage, policy, and inference all stay inside customer infrastructure.
The whole operational boundary moves local. Nothing needs to leave customer hardware unless the customer explicitly designs it that way.
Users access the local ChtSafe deployment inside the customer environment.
Policy, audit, and route logic remain on customer-controlled hardware.
Models and supporting services run inside the same controlled stack.
Keys, storage, deletion, and recovery policies stay under customer control.
Nothing by default if the deployment is designed for fully local operation.
The customer controls the full stack, not just inference.
Highly regulated, disconnected, or sovereignty-critical environments.
ChtSafe is sold in three operating models. Dedicated hardware and appliance packaging fit inside either Own Instance or full On-Premise depending on who controls the runtime, retention, and inference boundary.
| Mode | Hardware boundary | External provider dependency | Best fit |
|---|---|---|---|
| ChtSafe.com | Innovius-operated runtime, routing, and hosted retention boundary | Optional depending on the selected model mix | Smaller teams that want the fastest secure rollout |
| Own Instance | Customer-specific isolated instance; parts of routing, agents, or inference can run locally or in hybrid form | Optional and workload-dependent | Organizations that need separation and tighter control without full on-prem |
| On-Premise | Customer-controlled full stack, including runtime, storage, policy, and inference | None by default | Sovereignty-critical or disconnected deployments |
ChtSafe does not hide behind cloud logos or unsupported certification claims. The stronger case is architectural clarity, operational controls, and reviewable deployment boundaries.
On architecture: many organizations experience Microsoft-bundled AI as broad workspace access behind a unified identity and service boundary—readable content and powerful APIs by default. ChtSafe inverts that pattern with user-held keys, recommended client-side encryption, and least-privilege automation credentials so automation never receives a standing “decrypt everything” capability. That is intentionally closer in spirit to Apple’s Private Cloud Compute / Secure AI direction—purpose-bound, ephemeral server roles anchored to user devices—than to omnibus tenant AI tied to productivity suites.
Deployment-specific architecture notes, privacy boundaries, legal materials, and subprocessor context can be shared during procurement and security review—without badge theater.
Hosted, private, and on-prem modes map to workload risk instead of a one-size tenancy story. Customer prompts and files are not reused to train public models by default; commissioned fine-tuning stays customer-scoped.
No. Those controls matter, but they do not explain the request path. ChtSafe adds ShinrAI before provider inference, deployment-specific hardware boundaries, and explicit hosted/private/on-prem models for where trust actually changes.
No by default. Customer prompts, files, and workflow payloads are not automatically reused to improve general public models. Customer-specific fine-tuning work is commissioned separately and stays scoped to that customer deployment.
We can provide deployment-specific architecture explanations, privacy boundary notes, legal materials, subprocessor context, and additional operational details during security review. We do not replace that process with unsupported badge claims.
Microsoft-style bundles commonly centralize identity, content, and AI in one vendor-controlled plane; in practice that often means readable workspace data and wide API reach unless you layer extra controls yourself. ChtSafe’s design—client-side encryption optional by policy, keys not escrowed to operators, ShinrAI before inference, and automations limited to temporary derived subkeys and sub-API credentials per task—is meant to reduce that blast radius by construction, not by slogans. Apple’s public Secure AI / Private Cloud Compute narrative emphasizes device-anchored trust and ephemeral, purpose-specific server processing; our hosted and private paths aim at a similar separation of duties even though we are a different product and deployment surface. We spell out what leaves each boundary so you can map this to your threat model rather than compare logos.