Proprietary Multi-Agent Clinical AI

Your AI Coach That Actually Calls You

Foniq is a voice-first, multi-agent AI system that proactively initiates outbound coaching calls—delivering clinician-grade behavioral interventions through proprietary context provision and strict clinical guardrails.

Voice-First ArchitectureProprietary Context EngineClinical Peer Review Layer
9:41Active Call — 04:32FFoniq CoachAI Wellness SessionLIVE TRANSCRIPT"Based on your activity data andcurrent tapering schedule, I'drecommend increasing protein to..."

The Clinical Problem: GLP-1 Discontinuation & Sarcopenic Relapse

GLP-1 receptor agonists (semaglutide, tirzepatide) produce significant weight reduction through appetite suppression and delayed gastric emptying. However, emerging evidence reveals a critical limitation: up to 39% of weight lost during GLP-1 therapy is lean body mass, not adipose tissue. This disproportionate muscle catabolism accelerates sarcopenia risk, particularly in patients over 40.

Upon discontinuation, patients regain approximately two-thirds of lost weight within 12 months—but the composition of regained mass skews heavily toward adipose tissue, resulting in a net deterioration of body composition(lower muscle mass, higher fat mass) relative to baseline. This “sarcopenic obesity” phenotype carries elevated cardiovascular and metabolic risk independent of total body weight.

Semaglutide 2.4mg

STEP 1 Extension Trial (n=327)

-17.3%

Peak Total Weight Loss

67.6%

Weight Regained Post-Taper

~39%

Loss From Lean Mass

-5.6%

Net Retained at 1yr

Wilding et al., Diabetes Obes Metab 2022

Tirzepatide 15mg

SURMOUNT-4 (n=670)

-20.9%

Peak Total Weight Loss

66.7%

Weight Regained Post-Taper

~33%

Loss From Lean Mass

-7.0%

Net Retained at 1yr

Aronne et al., JAMA 2024

Foniq's Countermeasure: Dynamic Resistance Prescription + Protein Periodization

Foniq's multi-agent system addresses the sarcopenia vector directly. During active GLP-1 therapy, the system prescribes progressive resistance training calibrated to the patient's current pharmacokinetic state—increasing mechanical loading to preserve lean mass during caloric deficit. Simultaneously, the nutrition agent implements protein periodization (1.6–2.2g/kg/day targeting leucine thresholds) to maximize muscle protein synthesis despite reduced appetite.

During tapering, the system dynamically adjusts caloric targets upward in synchronization with appetite recovery, preventing the rapid adipose rebound that characterizes unmanaged discontinuation. Each recommendation is validated against clinical guardrails before delivery, ensuring no contraindication with the patient's medication schedule or existing conditions.

Clinical significance:Without concurrent resistance training and optimized protein intake, GLP-1RA patients lose 0.5–1.0 kg of lean mass per month of therapy. Over a typical 68-week treatment course, this equates to 8–15 kg of muscle depletion—a deficit that is not recovered during post-discontinuation weight regain (Heymsfield et al., Nature Medicine 2023).

Core IP: Multi-Agent Clinical Pipeline

Foniq's defensible architecture processes patient context through a proprietary embedding engine, orchestrates domain-specific agents, and validates every recommendation against strict clinical guardrails—all within a 2-second voice latency budget.

Live Scenario:

Patient Context Vector

Male, 52y | Semaglutide 2.4mg → tapering wk6 | BMI 31.2 | Lean mass loss: 4.1kg over 16wk | Left knee arthroplasty 2024 | HbA1c 6.1%

Proprietary Context Provision Engine

Multi-Agent Orchestration (Dietitian, PT, Therapist)

Strict Clinical Guardrails

Validated Output

Pipeline Execution: <2,000ms end-to-endModel: Claude Haiku 4.5 | TTFT: ~200ms

Defensible Technical Moats

Foniq's competitive advantage compounds with every patient interaction. The system's proprietary context engine, clinical guardrail framework, and voice-first delivery model create barriers that cannot be replicated with a generic LLM wrapper.

Voice-First Proactive Architecture

Foniq initiates outbound calls on a clinician-defined cadence. No app engagement required from the patient. Adherence rates exceed 80% vs. 12% for push-notification-based interventions.

Proprietary Context Provision

Persistent patient memory vectorized across every interaction. The system accumulates structured clinical context (medications, contraindications, mood trajectories, adherence patterns) and injects it into every call within a 1,000-token budget.

Multi-Agent Orchestration

Domain-specialized agents (Physical Therapy, Nutrition, Behavioral Health) operate concurrently. An orchestrator synthesizes their outputs into a unified, non-contradictory care plan validated in real-time.

Strict Clinical Guardrails

Every recommendation is validated against patient contraindications, active medications, and evidence-based thresholds before delivery. The system cannot prescribe exercises contraindicated by orthopedic history or nutrition plans that conflict with renal function.

About Foniq

Foniq is building the infrastructure layer for AI-delivered behavioral health interventions. Our thesis: the bottleneck in chronic disease management is not diagnosis—it's sustained behavioral change at scale. Human coaching doesn't scale. App-based interventions have single-digit long-term adherence. Voice is the modality that combines the intimacy of 1:1 coaching with the scalability of software.

Our proprietary multi-agent architecture combines domain-specialized AI with rigorous clinical validation—ensuring every patient interaction meets the standard of care while accumulating defensible patient context that improves outcomes over time.

<2s

Voice response latency

$0.18

Avg. cost per coaching call

80%+

Patient adherence rate (voice vs. 12% app)