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Trust · Responsible AI

Agentic AI with guardrails. Built for regulated environments.

73% autonomy with confidence-based escalation. Every interaction auditable, measurable, and defensible. You are not trading capability for safety.

See the decision flow Talk to our clinical lead
73% autonomy · Confidence-based escalation · Audit trails · PMS write-back
Four guardrails

Capability and safety, in the same platform. Not a trade-off.

Agentic AI for regulated environments doesn't mean dialled-down capability. It means four engineered guardrails that operate continuously — and four off-ramps to humans when they trip.

1

Confidence-based escalation.

Per-turn confidence scoring on transcript + intent. Above threshold the agent acts autonomously; below, it routes the conversation to a human queue with full context attached. The 73% / 27% split isn't a guess — it's the live operating point across deployed workflows.

73% autonomy · 27% human-routed
2

Audit trails on every interaction.

Every decision, escalation, transcript, and PMS / EHR write is logged with tamper-evident chain. 100% interaction coverage feeds SmartAnalytics. Customer-side SIEM forwarding supported. 7-year retention per HIPAA. Defensible end-to-end.

100% interaction coverage · 7-year retention
3

Bounded scope per workflow.

Each workflow has explicit boundaries: what the agent may say, what it must escalate, what it never claims. No clinical advice, no diagnosis, no dosing decisions. Out-of-scope intent triggers a documented off-ramp — not a model improvising.

0 clinical-advice incidents to date
4

Human-in-the-loop fallback.

Crisis language, clinical questions, billing disputes, and any patient opt-out hot-handoff to a human with full conversation context. Customer-side kill-switch on every shipped flow. Quarterly review by a standing clinical advisory bench.

100% opt-out compliance · kill-switch on every flow
See the decision flow in detail

Six checks before AI handles a patient call. Six exits before it doesn't.

The decision logic every Aqurio workflow runs through on every interaction — the lane where AI lives, and the off-ramps to a human when it shouldn't.

1

Disclose

Patient is told they're speaking with Aqurio AI, given the opt-out phrase, and asked for recording consent per state law.

2

Classify

Intent classifier routes the call: scheduling, billing, eligibility, prior-auth, clinical question, crisis, or other. Clinical / crisis → immediate escalation.

3

Score confidence

Per-turn confidence score on transcript + intent. Below threshold → escalate. Above → continue with audit-logged decision.

4

Apply guardrails

Workflow-specific safety filters: behavioral-health crisis keywords, suicide language, urgent symptoms, billing disputes — each with a dedicated escalation path.

5

Resolve or escalate

If in-scope and confident → resolve in conversation. Out of scope, low confidence, or guardrail-tripped → hot-handoff to staff queue with full context.

6

Audit

Every decision, escalation, and patient outcome logged. SmartAnalytics surfaces the trend; clinical advisory reviews edge cases quarterly.

Aqurio's live operating point across deployed workflows: 73% full autonomy, 27% confidence-routed to a human with full context attached. Crisis calls and clinical questions escalate at 100% by design — no exceptions, no learned behaviour to skip them.

Frequently asked questions

Common questions from chief medical officers.

Does Aqurio use patient data to train AI models?
No. Aqurio does not train base models on customer PHI. Customer data is used only to operate the workflows the customer has authorised. Workflow tuning and template adjustments happen on de-identified data with explicit customer opt-in.
How does Aqurio decide when to escalate to a human?
Every workflow has explicit escalation rules: clinical urgency keywords, billing disputes, low-confidence transcription, payer denials, behavioral-health crisis language, and any patient request for a human. Escalations route to the customer's queue with full conversation context attached.
Can patients opt out of AI?
Yes. Aqurio supports per-call opt-out ("I want to speak to a person" routes immediately to staff) and per-patient persistent opt-out (chart flag respected on all future inbound). Opt-out is also captured in SMS / chat workflows and respected across channels.
How does Aqurio handle clinical advice?
Aqurio does not give clinical advice. SmartAgent answers operational questions (scheduling, eligibility, prior-auth status, billing) and triages clinical questions to the appropriate care team queue — never to a diagnosis, dosing decision, or treatment recommendation. The triage logic is reviewed quarterly by our clinical advisory bench.
What model providers does Aqurio use?
Aqurio operates a multi-model architecture: enterprise-tier LLMs from OpenAI, Anthropic, and AWS Bedrock under BAA-covered contracts, plus Aqurio's own healthcare-tuned models for tasks like clinical-terminology recognition and payer-specific document assembly. Model routing is governed by per-task confidence and safety constraints.
How is bias monitored?
Aqurio runs continuous bias monitoring across language proficiency, accent, demographic markers, and outcome equity. Every quarter we publish a customer-facing fairness report comparing AI handle outcomes across patient cohorts; deviations beyond threshold trigger model retuning under our clinical advisory bench.
More questions? Talk to our clinical AI lead

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