A clinical team, in software
The same agent architecture operates across polyconditions — orchestration is condition-agnostic by design.
Education agent
RAG-trained personalized health literacy across all polyconditions. In an IRB-approved prospective study, patient knowledge scores improved from 72.8% to 88.4% (p<0.001) — preliminary data.
History agent
Conversational EMR augmentation. Longitudinal voice-based history capture across visits, labs, devices, and notes — reducing clinician documentation burden and building a living patient model.
Behavioral agent
Reinforcement learning for medication adherence and lifestyle modification. Target endpoint: 35% adherence improvement in actively enrolling RCT (preliminary; in collaboration with the University of Adelaide).
Clinical decision support agent
Real-time monitoring against condition-specific guidelines with multi-condition reasoning across comorbidities. FDA SaMD pre-submission targeted Q4 2026 (not yet cleared).
Operations agent
Scheduling, referrals, lab orders, and CMS billing documentation. Automates the qualifying activity log for RPM, CCM, APCM, and BHI codes — turning continuous care into auditable, billable work.
Clinical metrics referenced above represent preliminary data from ongoing studies and are not intended as evidence of safety or efficacy. AlfredAI is not FDA cleared; the SaMD pathway is targeted for filing in Q4 2026.