The Challenge
Existing health and nutrition apps focus on calorie counting — not health intelligence. Families with different dietary needs have no platform that adapts to each member individually while keeping everyone coordinated. The market has no solution that combines personalized nutrition, health education, clinical interoperability, and AI-powered insight — let alone one that serves consumers, health plans, and government agencies from the same architecture.
- No existing platform supports multiple dietary approaches within a single household
- Current tools track numbers without teaching the science behind them
- Most apps use shame-based compliance instead of building sustainable habits
- Health data stays siloed — patients can’t easily share nutrition data with providers, and providers have zero visibility between visits
- SNAP/EBT recipients have no free, evidence-based meal planning tools
- Building this scope as a solo founder requires a fundamentally different development approach
The AI Development Story
NourishIQ isn’t just a platform I designed — it’s a platform I’m building using AI-augmented development. This is the section that makes this case study unique: no other instructional design or change management professional has a case study about directing AI agents to build production software.
How I Build with AI Agents
I orchestrate parallel AI agents — multiple simultaneous development sessions, each focused on a different component — to architect and build a full-stack application. This isn’t “asking ChatGPT for help.” This is systematic, context-managed software development.
“I don’t just recommend AI adoption — I’ve lived it. I direct AI agents to build production-grade applications, which means I can design realistic adoption strategies because I understand the actual capabilities and limitations firsthand.”
What I Built
NourishIQ is a comprehensive health intelligence platform designed around how families actually eat. Rather than just tracking calories, it combines personalized dietary support, health education, clinical data interoperability, and AI-powered insight into a single experience.
Multi-Member Architecture
Each family member has individualized dietary settings while sharing a unified household meal plan and experience.
Health Intelligence Layer
Glucose and ketone tracking with proprietary biomarker correlation — surfacing meaningful health insights over time, not just numbers.
Leif AI Explorer Engine
Proprietary LLM fine-tuned on nutrition science with automatic complexity routing — from instant lookups to deep multi-source pattern discovery.
Integrated LMS
Structured education platform built with instructional design methodology — progressive learning pathways, not a bolted-on FAQ section.
Proprietary Engines
Four purpose-built engines: Correlation Engine, P:E Ratio Calculator, Ingredient Chemistry Engine, and Recipe Conversion Engine.
Clinical Compliance
HIPAA, FHIR R4, Blue Button 2.0, Smart Health Cards, NIST 800-63-3 identity, GINA genetic protections, and COPPA teen safety.
Tiered SaaS Model
Multi-tier subscription from free (Seedling) to premium family plans — with SNAP/EBT full access at every tier.
Behavior-First Design
Instructional design principles applied to gamification — celebrating consistency over perfection to drive sustainable adoption.
Cooking Skills Courses
Video-based courses spanning knife skills, nose-to-tail cooking, fermentation, and more — available on all plans including free.
Dynamic Theming Engine
UI system that adapts content and visual design contextually — demonstrating advanced front-end architecture.
Leif — AI Explorer Engine
Leif is Fortael’s proprietary AI engine — purpose-built to decipher health data by finding hidden patterns and connections in nutrition science. Where NourishIQ collects and visualizes your data, Leif navigates it.
- Built on open-source foundation models, fine-tuned on a proprietary nutrition science database — not an API wrapper
- RAG-augmented with curated evidence from USDA FoodData Central, peer-reviewed research, and dietary guidelines
- Automatic complexity routing delivers the right depth of analysis for every query — from instant lookups to deep multi-source pattern discovery
- Every response is grounded in cited sources — users always know whether Leif is citing research or connecting dots
- Supports all dietary approaches where evidence exists — will not diagnose conditions or prescribe medications
Leif represents the intelligence layer of the platform — translating raw health data into actionable, evidence-based insights that no single database or search engine could surface on its own.
Integrated LMS
NourishIQ includes an integrated Learning Management System built from the ground up using instructional design methodology. This isn’t a bolted-on FAQ section — it’s a structured education platform with tiered content delivery.
- Applied instructional systems design principles to create progressive learning pathways
- Designed tiered content architecture that maps to the platform’s subscription model
- Built evidence-based educational content using multimedia learning best practices
- Structured curriculum around competency-based progression, not just information delivery
This directly maps to my Master’s coursework at UCF — EME 6613 (Instructional Systems Design) and EME 6507 (Multimedia for Education & Training). I’m not just studying instructional design systems. I’m building one.
Clinical Compliance & Interoperability
NourishIQ is architected for clinical-grade data handling from the ground up — not bolted on after the fact. The compliance layer spans six major standards, enabling the platform to operate across consumer, healthcare, and government contexts.
HIPAA
3-tier data classification, AES-256-GCM encryption, immutable audit logging, 7-year retention
FHIR R4
Full clinical interoperability with SMART on FHIR, Blue Button 2.0, and Smart Health Cards
NIST 800-63-3
IAL2 identity proofing and AAL2 multi-factor authentication with passkeys
GINA
Genetic data as Tier 3 PHI — zero third-party sharing, raw data auto-deletion
COPPA
Age verification, non-overridable fasting caps for teens, parental consent gates
CMS Ecosystem
Kill the Clipboard flows, Medicare App Library pipeline, DiMe Seal vetting alignment
Three Audiences, One Architecture
NourishIQ is designed to serve consumers directly while also addressing the needs of health plans and government agencies — all from the same platform architecture.
Individuals & Families
Personalized meal planning, health tracking, cooking courses, and Leif-powered insights — with SNAP/EBT full access at every tier including free.
Health Plans
12 chronic condition mappings, 5 HEDIS Star Measures, FHIR data sharing with care teams, and between-visit member visibility. Three partnership models.
Government & CMS
MAHA Commission alignment, Kill the Clipboard FHIR flows, Diabetes & Obesity Prevention capability, and Medicare App Library pipeline.
Food Access & Equity
NourishIQ provides full access to evidence-based meal planning for SNAP/EBT recipients at every subscription tier, including free. This isn’t a stripped-down version — it’s the same core meal planning, cooking courses, and food science education available to all users.
- SNAP/EBT eligibility verification with full feature access on the free tier
- Budget-optimized meal planning designed for real-food nutrition at scale
- Cooking skills courses available to all plans — knife skills, budget cooking, fermentation, and more
- Aligned with USDA dietary guidelines and partnership pathways
Science & Evidence Foundation
NourishIQ’s approach is built on a 140+ year evidence timeline spanning peer-reviewed research, clinical trials, and foundational nutrition science — from Dr. John Rollo’s first documented dietary intervention in 1797 through modern RCTs at Harvard, Stanford, and the University of Edinburgh.
- 30+ publications backing the research foundation
- 8 institutional research foundations cited with canonical researchers
- Active clinical trials at Harvard, Stanford, and Edinburgh informing the evidence base
- Evidence-based methodology — every claim grounded in published research with full citations
Key Takeaway
“Technology adoption isn’t a slide deck problem — it’s a build problem.”
Applied here: I didn’t just study AI adoption. I adopted AI to build a production platform.I’ve spent 25 years building things — trade schools, software systems, training programs, marketing campaigns, operational workflows. Fortael and NourishIQ represent the full synthesis: identify a gap, learn the domain, build the solution, and architect it for scale across consumers, enterprise, and government. AI-augmented development didn’t change what I do. It accelerated how fast I can do it.