Digital Wellness Academy (DWA)

A compounding clinical intelligence engine for behavioral health — not a wrapper around an LLM.

Digital Wellness Academy is a PHI-safe, AI-powered mental health education platform with an embedded AI wellness coach, 922 evidence-linked lessons, and a clinician portal that surfaces real-time patient progress. Built as the behavioral health vertical on the SoloFrame Mono-PaaS engine, DWA delivers between-session care that clinical research shows is independently predictive of better patient outcomes — while a five-stage AI retraining flywheel ensures the platform grows smarter with every interaction.

Mental health treatment most commonly fails between sessions — when patients are alone, without the tools or support to practice what they're learning. Digital Wellness Academy closes that gap. It is a full-stack behavioral health platform combining a 922-lesson evidence-aligned curriculum (CBT, DBT, evidence grades from NICE 2024 and Cochrane 2026), an AI wellness coach with per-turn distress classification, 23 validated clinical assessments (PHQ-9, GAD-7, and 19 others), and a clinician portal that surfaces patient progress, mood timelines, and AI-generated session-prep briefs.

The platform is built as the PHI-bearing vertical on the SoloFrame Mono-PaaS engine — a reusable, compliance-aware infrastructure that proves "compliance is a manifest flag, not a fork." DWA's MAIA distress classifier runs on every coaching turn, a per-turn state machine prevents unsafe responses even under model failure, and a human-in-the-loop retrain loop means the safety architecture improves continuously as clinicians label classifier outputs in production. The platform's six-layer cost architecture delivers a measured 4.4× per-session AI cost reduction — making clinical-grade mental health AI economically sustainable at the practice level.

Developed by SVTech Consulting, LLC, with Real Psychiatric Services as the originating clinical partner. Targeting behavioral health networks, university counseling centers, and enterprise behavioral health benefits providers as Stage-1 pilots. Active beta, May 2026.

• Safety is a pipeline, not a model choice. MAIA distress classifier on every coaching turn, fail-safe closed routing (MAIA outage → 988, never an unguarded LLM call), per-turn state machine with crisis recall = 1.000.
• PHI compliance is a manifest flag. DWA and GTM-OS share the same engine with different manifests — proving the platform's central pitch: one engine, two products, compliance is a flag, not a fork.
• Intelligence compounds with scale. Five-stage MAIA retrain loop (inference capture → clinical spot-check → eval datasets → per-version rollups → hot-swap) means the classifier improves with every labeled patient interaction.
• Content-driven data flywheel. 2,400+ RAG chunks grow in retrieval quality as real patient queries refine the signal. No manual model update required.
• Clinician-in-the-loop by design. NPI-verified provider portal, PMHNP-curated crisis response templates, session-prep briefs, and alert episodes — not just an AI replacing clinical judgment, but AI making clinician judgment faster and better-informed.
• Cost-aware architecture. Six-layer cost stack (tier routing, OpenRouter model bus, prompt caching, RAG, CPU-side classifier, fine-tune roadmap) delivers 4.4× per-session AI cost reduction — enabling sustainable per-practice pricing.

• Active beta deployment on SoloFrame Mono-PaaS engine, May 2026.
• First paying clinical customer: Real Psychiatric Services (3-practitioner behavioral health office, Ohio).
• 123 DWA-specific tests passing; engineering infrastructure fully operational (MAIA, RAG, assessments, alert workflow, provider portal).
• Targeting Stage-1 marquee pilot: 10+ provider behavioral health networks, university counseling centers, or enterprise behavioral health benefits providers.
• Layer-6 fine-tune roadmap unblocked — projected 6.7× cost reduction (conservative) with Nebius training infrastructure.

DWA is designed to run at the intersection of clinical safety and GPU-scale AI — and Nebius is the infrastructure layer that makes Layer-6 viable. The platform's six-layer cost architecture currently delivers a 4.4× per-session reduction using OpenRouter-routed inference and a CPU-side distress classifier. The documented Layer-6 fine-tune roadmap (deferred, pending Nebius account and customer training data) projects a further 6.7–13.6× reduction as custom-trained models replace general-purpose endpoints.

More broadly, Nebius GPU infrastructure enables the MAIA retrain cadence at production scale: as clinical spot-check labels accumulate across a multi-practice deployment, continuous fine-tuning of the distress classifier is the event that turns the safety architecture from "defensible at launch" to "meaningfully better than any single-session tool" over time.