Instruction-Tuned
Intelligence
Agents are pre-trained on healthcare standards like ICD, CPT, and CMS policies, fine-tuned with your organization's specific needs. Ability to pass custom instructions, prompts, and labeled data—capturing local rules, workflows, and edge cases. This enables the agent to learn & adapt continuously, aligning with evolving internal logic, clinical operations, and team feedback.

Manage
Hallucinations 
Every agent response undergoes a rigorous two-step process. First, the raw output from the LLM is validated using techniques such as self-consistency checks, retrieval-based grounding, knowledge base alignment, and confidence estimation. Then, the validated result is refined by healthcare-specific rules or human-in-the-loop feedback—ensuring decisions are safe, traceable, and grounded in fact.

Federated Data Access
& Governance
Agents securely connect to EHRs, payer systems, clinical documents, imaging, and unstructured text—without compromising HIPAA or local data governance. By maintaining source-level data ownership and respecting access controls, agents surface real-time insights across silos while ensuring compliance, traceability, and auditability.

Workflow
& Rule Orchestration
Agents coordinate complex workflows like prior authorizations, quality reporting, and referrals—based on rules you define. Teams can pass inputs like “run,” “edit,” or “escalate,” making the automation dynamic and interactive. With flexible triggers, override logic, and built-in transparency, users stay in control of every step—giving your team full control over automation.

Responsible
& Compliant AI
Agents are designed with layered guardrails: policy constraints, input/output validation, fallback behaviors, restricted function access, and full trace logging. These measures enforce transparency, prevent unintended actions, and support HIPAA/CMS compliance. By combining runtime governance with auditability, agents remain safe, fair, and aligned with institutional responsibility.

API & Natural
Language Interfaces
Agents are available through FHIR-compatible APIs or natural language interfaces—providing structured responses for system-to-system automation or freeform replies for chat-based interaction. This dual-mode delivery supports both developer integration and day-to-day clinical or operations use, enabling fast adoption across technical and non-technical teams.