Meet Orbital.

Orbital is Aona’s own security technology: purpose-built models, deterministic detectors and the evaluation work behind them. It powers sensitive-data recognition and policy checks inside Aona, with supported processing on employee machines, on-premises or in the cloud. Orbital Edge, Orbital Base and Orbital Cloud are the three Models. Radar, Lens, Compass, Reason and Vision are specialist Submodels inside the suite; availability depends on the release and configuration.

Orbital’s role here is AI security: detecting sensitive data and checking requests against policies in supported employee or agent workflows. It is not a replacement for the AI tools your organisation uses to write, research or code.

Orbital runs Aona’s own model inference instead of requiring a paid general-purpose AI API call for every Orbital check. Deterministic checks need no model inference; focused models handle recognition and policy context. This architecture is designed to reduce inference spend. Actual savings depend on the workload and deployment; compute, hosting and Aona service costs still apply.

Structured identifiers need validation. Personal data needs recognition. Business policies need context. Orbital brings these different techniques into one security system. Aona evaluates both missed detections and false positives using labelled examples, hard negatives and held-out tests. For your policies, test separate allowed and disallowed requests on the intended Model, language and deployment. This is an engineering approach to accuracy, not a promise of perfect detection or a universal benchmark score.

On Aona’s historical July 2026 internal benchmark of 887 synthetic policy checks, the Orbital Cloud v8 candidate matched 99.10% of reference decisions, versus 80.50% for Granite Guardian 3.1 2B and 76.32% for Llama Guard 3 1B. Orbital was trained on that older policy distribution; the alternatives were zero-shot and were faster in this test. These are not current-product guarantees or a ranking against general-purpose AI models. Review the full accuracy and latency results, then evaluate your own workload.

No. Policy accuracy measures reference flag-or-not-flag decisions. The separate Lens 1.9.2 test measured false sensitive-data detections on benign documents: 2 of 240, compared with 68 for Lens 1.9.1. That is not a general hallucination rate. No matched cost-per-request comparison is available here. Orbital avoids a separate general-purpose AI API call for its own inference; compute, hosting and Aona service costs still apply.

Orbital supports deterministic rules and model-based checks. Use explicit patterns, term lists and checksum validation when the rule is precise; use learned recognition or Reason when meaning and policy context matter. Model-based does not necessarily mean random: inference can be repeatable, but a learned verdict still needs evaluation. Configure the supported checks for your task; this is not a promise of automatic escalation whenever a model is uncertain.

Orbital runs Aona’s own model inference instead of requiring a paid general-purpose AI API call for every Orbital check. Deterministic checks need no model inference; focused models handle recognition and policy context. This architecture is designed to reduce inference spend. Actual savings depend on the workload and deployment; compute, hosting and Aona service costs still apply. Deterministic-only operation avoids model inference, and selecting supported checks lets you avoid unnecessary model work. Compare total costs, including infrastructure and service costs, on representative request volumes; there is no universal savings percentage.

Orbital can identify sensitive data and evaluate requests against configured policies. On a supported Aona client path, those findings can inform redaction or blocking. For an API integration, your application must apply the returned verdict. A model check alone does not intercept every AI request.

Yes. Aona supports customer-authored Reason guardrails with policy instructions and labelled examples, plus Radar patterns and term lists. This configures the check; it is not per-customer model retraining. Use separate test requests to evaluate allowed and disallowed behaviour before activation, and confirm support in your deployment.

No. Supported Orbital checks can run on the user device, but that does not establish the location of all backend processing, logging or telemetry. If an allowed request is sent to a third-party AI provider, that provider’s data handling remains separate. Confirm the complete workflow, not just the model location.

Reason’s capacity is what distinguishes the three Orbital Models. Radar, Lens, Compass and Vision are shared Submodels; a larger Model is not a separate upgrade to every detector. Orbital Cloud is server-side only. Model selection does not imply feature parity across clients or deployments, or a guaranteed accuracy or latency result.

Aona offers separate choices for backend hosting and prompt processing. The backend can run in your cloud, on-premises or on Aona-managed servers. Prompt processing can run on the user device, in your environment or on Aona-managed servers. Confirm the supported Orbital Model, feature and integration configuration for your deployment.

Orbital Files prepares text from supported documents and OCR paths for configured guardrails. It is a separate processing pipeline, not an Orbital Model. Vision inspects original pixels for signature-shaped regions and remains staging-only, not generally available. Neither signature detection nor a policy verdict proves identity, consent or compliance. Confirm file formats, original-byte access and the processing path.

The documented release-quality language scope is English, French and Spanish. Other languages are best effort and need workflow-specific evaluation. Test real language patterns, document types and policy examples from your organisation; release-quality scope is not a guarantee of perfect results.

No. Compass’s use-case analytics describes the activity behind a request, such as research or email. It is separate from the configured guardrail verdict. The documented API integration is flag-gated staging, not generally available; confirm its availability for your deployment.